Create_animation - Copy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dependencies.NN import *\n",
    "from dependencies.LTL import *\n",
    "\n",
    "\n",
    "from dependencies.csrl.mdp import GridMDP\n",
    "from dependencies.csrl.oa import OmegaAutomaton\n",
    "from dependencies.csrl import ControlSynthesis\n",
    "import numpy as np\n",
    "import os\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Omega-automaton states (including the trap state): 5\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### from the Nursary case ###\n",
    "# LTL Specification\n",
    "# ltl = ('G ('\n",
    "#     '(!d) & '\n",
    "#     '((b & (!(X b)))->(X ((!b) U (a|c)))) & '\n",
    "#     '(((!b) & (X b) & (!(X X b)))->((!a) U c)) & '\n",
    "#     '(a->(X ((!a) U b))) & '\n",
    "#     '(c->((!a) U b)) & '\n",
    "#     '((b & (X b))->(F a))'\n",
    "# ')')\n",
    "\n",
    "ltl = (\"(G !d) & ((!c) U b) & ((!b) U a) & (F c)\")\n",
    "\n",
    "# Translate the LTL formula to an LDBA\n",
    "oa = OmegaAutomaton(ltl)\n",
    "print('Number of Omega-automaton states (including the trap state):',oa.shape[1])\n",
    "\n",
    "# MDP Description\n",
    "shape = (5,5)\n",
    "# E: Empty, T: Trap, B: Obstacle\n",
    "structure = np.array([\n",
    "['E',  'E',  'E',  'E',  'E'],\n",
    "['E',  'B',  'E',  'E',  'E'],\n",
    "['E',  'E',  'E',  'E',  'E'],\n",
    "['E',  'E',  'E',  'E',  'E'],\n",
    "['E',  'E',  'E',  'E',  'E']\n",
    "])\n",
    "\n",
    "# Labels of the states\n",
    "label = np.array([\n",
    "[(),    (),    (),    (),    ()],\n",
    "[('c',),(),    ('d',),(),    ()],\n",
    "[(),    (),    (),    (),    ('a',)],\n",
    "[(),    ('d',),(),    ('d',),()],\n",
    "[(),    ('b',),(),    (),    ()]\n",
    "],dtype=object)\n",
    "# Colors of the labels\n",
    "lcmap={\n",
    "    ('a',):'yellow',\n",
    "    ('b',):'greenyellow',\n",
    "    ('c',):'turquoise',\n",
    "    ('d',):'pink'\n",
    "}\n",
    "grid_mdp = GridMDP(shape=shape,structure=structure,label=label,lcmap=lcmap, p=1, figsize=5)  # Use figsize=4 for smaller figures\n",
    "grid_mdp.plot()\n",
    "\n",
    "# Construct the product MDP\n",
    "csrl = ControlSynthesis(grid_mdp,oa)\n",
    "max_rew = round(csrl.reward.max(), 3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-09-11 14:44:40.492647: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE4.1 SSE4.2 AVX AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-09-11 14:44:40.493125: I tensorflow/core/common_runtime/process_util.cc:146] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)           [(None, 5, 5, 5)]    0           []                               \n",
      "                                                                                                  \n",
      " conv2d (Conv2D)                (None, 5, 5, 32)     672         ['input_1[0][0]']                \n",
      "                                                                                                  \n",
      " conv2d_1 (Conv2D)              (None, 5, 5, 8)      1032        ['conv2d[0][0]']                 \n",
      "                                                                                                  \n",
      " flatten (Flatten)              (None, 200)          0           ['conv2d_1[0][0]']               \n",
      "                                                                                                  \n",
      " dense (Dense)                  (None, 32)           6432        ['flatten[0][0]']                \n",
      "                                                                                                  \n",
      " dense_1 (Dense)                (None, 16)           528         ['dense[0][0]']                  \n",
      "                                                                                                  \n",
      " dense_2 (Dense)                (None, 9)            153         ['dense_1[0][0]']                \n",
      "                                                                                                  \n",
      " dense_3 (Dense)                (None, 1)            17          ['dense_1[0][0]']                \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 8,834\n",
      "Trainable params: 8,834\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = build_model(csrl.ch_states[(0,0,0,0)].shape, csrl.shape[-1])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = \"[] ~d /\\ (~c % b) /\\ (~b % a) /\\ (<> c)\"\n",
    "\n",
    "LTL_formula = parser.parse(t)\n",
    "predicates={'a':[14], 'b':[21], 'c':[5], 'd':[7,16,18]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('/\\\\',\n",
       " ('[]', ('~', (None, 'd'))),\n",
       " ('/\\\\',\n",
       "  ('%', ('~', (None, 'c')), (None, 'b')),\n",
       "  ('/\\\\', ('%', ('~', (None, 'b')), (None, 'a')), ('<>', (None, 'c')))))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LTL_formula"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model(csrl.ch_states[(0,0,0,0)][np.newaxis])\n",
    "\n",
    "N, W, Q, P = np.zeros(csrl.shape), np.zeros(csrl.shape), np.zeros(csrl.shape), np.zeros(csrl.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trajectory: [0, 0, 5, 5, 10, 11, 12, 13, 14, 9, 14, 14, 19, 24, 19, 24]\n",
      "action_history: [3, 1, 3, 1, 2, 2, 2, 2, 0, 1, 2, 1, 1, 0, 1]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 2, 4), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 3, 4), (0, 4, 4, 4)]\n",
      "----------\n",
      "trajectory: [0, 0, 0, 5, 5, 10, 11, 11, 12, 11, 12, 13, 14, 19, 24, 23]\n",
      "action_history: [0, 3, 1, 3, 1, 2, 0, 2, 3, 2, 2, 2, 1, 1, 3]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 0), (0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3)]\n",
      "----------\n",
      "trajectory: [0, 5, 5, 0, 1, 1, 1, 0, 5, 10, 5, 0, 0, 0, 1, 2]\n",
      "action_history: [1, 3, 0, 2, 1, 0, 3, 1, 1, 0, 0, 0, 3, 2, 2]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 1, 0), (0, 4, 0, 0), (0, 4, 0, 1), (0, 4, 0, 1), (0, 4, 0, 1), (0, 4, 0, 0), (0, 4, 1, 0), (0, 4, 2, 0), (0, 4, 1, 0), (0, 4, 0, 0), (0, 4, 0, 0), (0, 4, 0, 0), (0, 4, 0, 1), (0, 4, 0, 2)]\n",
      "----------\n",
      "trajectory: [0, 5, 0, 0, 1, 0, 1, 1, 2, 2, 3, 2, 3, 8, 9, 8]\n",
      "action_history: [1, 0, 0, 2, 3, 2, 1, 2, 0, 2, 3, 2, 1, 2, 3]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 0, 0), (0, 4, 0, 0), (0, 4, 0, 1), (0, 4, 0, 0), (0, 4, 0, 1), (0, 4, 0, 1), (0, 4, 0, 2), (0, 4, 0, 2), (0, 4, 0, 3), (0, 4, 0, 2), (0, 4, 0, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 1, 3)]\n",
      "----------\n",
      "trajectory: [0, 5, 5, 10, 11, 12, 13, 14, 19, 24, 23, 23, 22, 21, 20, 21]\n",
      "action_history: [1, 3, 1, 2, 2, 2, 2, 1, 1, 3, 1, 3, 3, 3, 2]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0), (0, 4, 4, 1)]\n",
      "----------\n",
      "trajectory: [0, 5, 0, 1, 1, 2, 2, 3, 8, 9, 4, 4, 4, 3, 8, 9]\n",
      "action_history: [1, 0, 2, 1, 2, 0, 2, 1, 2, 0, 2, 0, 3, 1, 2]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 0, 0), (0, 4, 0, 1), (0, 4, 0, 1), (0, 4, 0, 2), (0, 4, 0, 2), (0, 4, 0, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 0, 4), (0, 4, 0, 4), (0, 4, 0, 4), (0, 4, 0, 3), (0, 4, 1, 3), (0, 4, 1, 4)]\n",
      "----------\n",
      "trajectory: [0, 5, 10, 11, 12, 13, 14, 19, 24, 23, 22, 21, 20, 21, 16]\n",
      "action_history: [1, 1, 2, 2, 2, 2, 1, 1, 3, 3, 3, 3, 2, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0), (0, 4, 4, 1), (0, 4, 3, 1)]\n",
      "----------\n",
      "trajectory: [0, 0, 5, 10, 11, 12, 13, 8, 9, 14, 9, 14, 19, 24, 23, 22]\n",
      "action_history: [3, 1, 1, 2, 2, 2, 0, 2, 1, 0, 1, 1, 1, 3, 3]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2)]\n",
      "----------\n",
      "trajectory: [0, 5, 10, 11, 12, 13, 14, 19, 24, 23, 22, 23, 22, 23, 22, 23]\n",
      "action_history: [1, 1, 2, 2, 2, 2, 1, 1, 3, 3, 2, 3, 2, 3, 2]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 3)]\n",
      "----------\n",
      "trajectory: [0, 5, 10, 11, 12, 13, 8, 3, 8, 7]\n",
      "action_history: [1, 1, 2, 2, 2, 0, 0, 1, 3]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 1, 3), (0, 4, 0, 3), (0, 4, 1, 3), (0, 4, 1, 2)]\n",
      "----------\n",
      "trajectory: [0, 5, 10, 11, 12, 13, 14, 19, 24, 23, 22, 17, 22, 21, 20, 15]\n",
      "action_history: [1, 1, 2, 2, 2, 2, 1, 1, 3, 3, 0, 1, 3, 3, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 3, 2), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0), (0, 4, 3, 0)]\n",
      "----------\n",
      "trajectory: [0, 0, 0, 5, 10, 10, 11, 12, 13, 8, 9, 14, 19, 24, 23, 22]\n",
      "action_history: [3, 0, 1, 1, 3, 2, 2, 2, 0, 2, 1, 1, 1, 3, 3]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 0), (0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2)]\n",
      "----------\n",
      "trajectory: [0, 5, 5, 10, 11, 12, 13, 8, 9, 14, 19, 24, 23, 22, 21, 20]\n",
      "action_history: [1, 3, 1, 2, 2, 2, 0, 2, 1, 1, 1, 3, 3, 3, 3]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0)]\n",
      "----------\n",
      "trajectory: [0, 5, 10, 11, 12, 13, 8, 9, 9, 14, 19, 24, 23, 22, 21, 20]\n",
      "action_history: [1, 1, 2, 2, 2, 0, 2, 2, 1, 1, 1, 3, 3, 3, 3]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0)]\n",
      "----------\n",
      "trajectory: [0, 0, 1, 0, 0, 5, 10, 11, 12, 13, 8, 9, 14, 19, 24, 23]\n",
      "action_history: [0, 2, 3, 0, 1, 1, 2, 2, 2, 0, 2, 1, 1, 1, 3]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 0), (0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3)]\n",
      "----------\n",
      "trajectory: [0, 5, 10, 5, 5, 10, 11, 12, 13, 8, 9, 14, 19, 24, 23, 22]\n",
      "action_history: [1, 1, 0, 2, 1, 2, 2, 2, 0, 2, 1, 1, 1, 3, 3]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 1, 0), (0, 4, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2)]\n",
      "----------\n",
      "trajectory: [0, 5, 10, 11, 12, 13, 8, 9, 14, 19, 24, 23, 22, 21, 20, 15]\n",
      "action_history: [1, 1, 2, 2, 2, 0, 2, 1, 1, 1, 3, 3, 3, 3, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0), (0, 4, 3, 0)]\n",
      "----------\n",
      "trajectory: [0, 5, 10, 11, 12, 13, 8, 9, 14, 19, 24, 23, 22, 21, 20, 15]\n",
      "action_history: [1, 1, 2, 2, 2, 0, 2, 1, 1, 1, 3, 3, 3, 3, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0), (0, 4, 3, 0)]\n",
      "----------\n",
      "trajectory: [0, 5, 10, 11, 12, 13, 14, 19, 24, 23, 22, 21, 22, 21, 20, 15]\n",
      "action_history: [1, 1, 2, 2, 2, 2, 1, 1, 3, 3, 3, 2, 3, 3, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0), (0, 4, 3, 0)]\n",
      "----------\n",
      "trajectory: [0, 5, 5, 5, 10, 11, 12, 13, 8, 9, 14, 19, 24, 23, 22, 21]\n",
      "action_history: [1, 2, 2, 1, 2, 2, 2, 0, 2, 1, 1, 1, 3, 3, 3]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 1, 0), (0, 4, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1)]\n",
      "----------\n",
      "Wins: 0 / 20\n",
      "last reward: 0.0   | trajectory: [0, 5, 5, 5, 10, 11, 12, 13, 8, 9, 14, 19, 24, 23, 22, 21]\n",
      "Actions: [1, 2, 2, 1, 2, 2, 2, 0, 2, 1, 1, 1, 3, 3, 3]\n",
      "trajectory: [0, 0, 5, 10, 10, 11, 12, 13, 8, 9, 14, 19, 24, 23, 22, 21]\n",
      "action_history: [3, 1, 1, 3, 2, 2, 2, 0, 2, 1, 1, 1, 3, 3, 3]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1)]\n",
      "----------\n",
      "trajectory: [0, 5, 0, 5, 10, 10, 11, 11, 12, 13, 14, 19, 24, 23, 22, 21]\n",
      "action_history: [1, 0, 1, 1, 3, 2, 0, 2, 2, 2, 1, 1, 3, 3, 3]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 0, 0), (0, 4, 1, 0), (0, 4, 2, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1)]\n",
      "----------\n",
      "trajectory: [0, 5, 10, 11, 12, 13, 8, 9, 14, 19, 24, 23, 22, 21, 16]\n",
      "action_history: [1, 1, 2, 2, 2, 0, 2, 1, 1, 1, 3, 3, 3, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 3, 1)]\n",
      "----------\n",
      "trajectory: [0, 0, 5, 0, 1, 2, 3, 8, 9, 14, 19, 24, 23, 22, 17, 22]\n",
      "action_history: [0, 1, 0, 2, 2, 2, 1, 2, 1, 1, 1, 3, 3, 0, 1]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 0, 0), (0, 4, 0, 1), (0, 4, 0, 2), (0, 4, 0, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 3, 2), (0, 4, 4, 2)]\n",
      "----------\n",
      "trajectory: [0, 0, 5, 10, 11, 12, 13, 14, 19, 24, 23, 22, 21, 20, 21, 22]\n",
      "action_history: [3, 1, 1, 2, 2, 2, 2, 1, 1, 3, 3, 3, 3, 2, 2]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0), (0, 4, 4, 1), (0, 4, 4, 2)]\n",
      "----------\n",
      "trajectory: [0, 5, 10, 11, 11, 12, 13, 14, 19, 24, 23, 22, 21, 20, 15, 20]\n",
      "action_history: [1, 1, 2, 0, 2, 2, 2, 1, 1, 3, 3, 3, 3, 0, 1]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0), (0, 4, 3, 0), (0, 4, 4, 0)]\n",
      "----------\n",
      "trajectory: [0, 5, 10, 11, 12, 13, 8, 9, 14, 19, 24, 23, 22, 21, 20, 20]\n",
      "action_history: [1, 1, 2, 2, 2, 0, 2, 1, 1, 1, 3, 3, 3, 3, 1]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0), (0, 4, 4, 0)]\n",
      "----------\n",
      "trajectory: [0, 5, 10, 11, 12, 13, 14, 19, 24, 23, 22, 22, 21, 20, 21, 20]\n",
      "action_history: [1, 1, 2, 2, 2, 2, 1, 1, 3, 3, 1, 3, 3, 2, 3]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0), (0, 4, 4, 1), (0, 4, 4, 0)]\n",
      "----------\n",
      "trajectory: [0, 5, 10, 11, 12, 13, 14, 19, 24, 23, 22, 21, 20, 20, 21, 22]\n",
      "action_history: [1, 1, 2, 2, 2, 2, 1, 1, 3, 3, 3, 3, 1, 2, 2]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0), (0, 4, 4, 0), (0, 4, 4, 1), (0, 4, 4, 2)]\n",
      "----------\n",
      "trajectory: [0, 5, 10, 11, 12, 13, 8, 9, 14, 19, 24, 23, 22, 21, 20, 20]\n",
      "action_history: [1, 1, 2, 2, 2, 0, 2, 1, 1, 1, 3, 3, 3, 3, 1]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0), (0, 4, 4, 0)]\n",
      "----------\n",
      "trajectory: [0, 5, 10, 11, 12, 13, 14, 19, 24, 23, 22, 17, 12, 13, 14, 19]\n",
      "action_history: [1, 1, 2, 2, 2, 2, 1, 1, 3, 3, 0, 0, 2, 2, 1]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 3, 2), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 2, 4), (0, 4, 3, 4)]\n",
      "----------\n",
      "trajectory: [0, 5, 10, 11, 12, 13, 8, 9, 14, 19, 24, 23, 22, 21, 20, 20]\n",
      "action_history: [1, 1, 2, 2, 2, 0, 2, 1, 1, 1, 3, 3, 3, 3, 1]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0), (0, 4, 4, 0)]\n",
      "----------\n",
      "trajectory: [0, 0, 5, 10, 11, 12, 13, 8, 9, 14, 19, 24, 23, 22, 21, 20]\n",
      "action_history: [0, 1, 1, 2, 2, 2, 0, 2, 1, 1, 1, 3, 3, 3, 3]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0)]\n",
      "----------\n",
      "trajectory: [0, 5, 10, 11, 12, 13, 14, 19, 24, 23, 22, 21, 20, 15, 20, 15]\n",
      "action_history: [1, 1, 2, 2, 2, 2, 1, 1, 3, 3, 3, 3, 0, 1, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0), (0, 4, 3, 0), (0, 4, 4, 0), (0, 4, 3, 0)]\n",
      "----------\n",
      "trajectory: [0, 0, 5, 0, 1, 1, 0, 1, 2, 3, 3, 8, 9, 14, 19, 24]\n",
      "action_history: [3, 1, 0, 2, 0, 3, 2, 2, 2, 0, 1, 2, 1, 1, 1]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 0, 0), (0, 4, 0, 1), (0, 4, 0, 1), (0, 4, 0, 0), (0, 4, 0, 1), (0, 4, 0, 2), (0, 4, 0, 3), (0, 4, 0, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4)]\n",
      "----------\n",
      "trajectory: [0, 5, 10, 11, 12, 13, 12, 13, 14, 19, 24, 23, 22, 21, 20, 21]\n",
      "action_history: [1, 1, 2, 2, 2, 3, 2, 2, 1, 1, 3, 3, 3, 3, 2]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0), (0, 4, 4, 1)]\n",
      "----------\n",
      "trajectory: [0, 5, 10, 11, 12, 13, 8, 9, 14, 19, 24, 23, 22, 21, 20, 20]\n",
      "action_history: [1, 1, 2, 2, 2, 0, 2, 1, 1, 1, 3, 3, 3, 3, 3]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0), (0, 4, 4, 0)]\n",
      "----------\n",
      "trajectory: [0, 0, 5, 0, 0, 0, 1, 2, 3, 8, 9, 4, 3, 2, 3, 8]\n",
      "action_history: [3, 1, 0, 0, 3, 2, 2, 2, 1, 2, 0, 3, 3, 2, 1]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 0, 0), (0, 4, 0, 0), (0, 4, 0, 0), (0, 4, 0, 1), (0, 4, 0, 2), (0, 4, 0, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 0, 4), (0, 4, 0, 3), (0, 4, 0, 2), (0, 4, 0, 3), (0, 4, 1, 3)]\n",
      "----------\n",
      "trajectory: [0, 5, 10, 11, 12, 13, 8, 9, 14, 19, 24, 23, 22, 21, 20, 21]\n",
      "action_history: [1, 1, 2, 2, 2, 0, 2, 1, 1, 1, 3, 3, 3, 3, 2]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0), (0, 4, 4, 1)]\n",
      "----------\n",
      "trajectory: [0, 5, 10, 11, 12, 13, 8, 9, 14, 19, 24, 23, 22, 17, 12, 13]\n",
      "action_history: [1, 1, 2, 2, 2, 0, 2, 1, 1, 1, 3, 3, 0, 0, 2]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 3, 2), (0, 4, 2, 2), (0, 4, 2, 3)]\n",
      "----------\n",
      "Wins: 0 / 20\n",
      "last reward: 0.0   | trajectory: [0, 5, 10, 11, 12, 13, 8, 9, 14, 19, 24, 23, 22, 17, 12, 13]\n",
      "Actions: [1, 1, 2, 2, 2, 0, 2, 1, 1, 1, 3, 3, 0, 0, 2]\n",
      "trajectory: [0, 0, 5, 10, 11, 11, 12, 13, 14, 19, 24, 23, 22, 21, 20, 15]\n",
      "action_history: [3, 1, 1, 2, 0, 2, 2, 2, 1, 1, 3, 3, 3, 3, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0), (0, 4, 3, 0)]\n",
      "----------\n",
      "trajectory: [0, 5, 0, 1, 0, 1, 2, 3, 2, 2, 3, 8, 9, 14, 19, 24]\n",
      "action_history: [1, 0, 2, 3, 2, 2, 2, 3, 0, 2, 1, 2, 1, 1, 1]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 0, 0), (0, 4, 0, 1), (0, 4, 0, 0), (0, 4, 0, 1), (0, 4, 0, 2), (0, 4, 0, 3), (0, 4, 0, 2), (0, 4, 0, 2), (0, 4, 0, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4)]\n",
      "----------\n",
      "trajectory: [0, 5, 0, 1, 2, 3, 2, 2, 3, 8, 9, 4, 4, 4, 9, 14]\n",
      "action_history: [1, 0, 2, 2, 2, 3, 0, 2, 1, 2, 0, 0, 2, 1, 1]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 0, 0), (0, 4, 0, 1), (0, 4, 0, 2), (0, 4, 0, 3), (0, 4, 0, 2), (0, 4, 0, 2), (0, 4, 0, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 0, 4), (0, 4, 0, 4), (0, 4, 0, 4), (0, 4, 1, 4), (0, 4, 2, 4)]\n",
      "----------\n",
      "trajectory: [0, 5, 10, 10, 11, 12, 13, 14, 19, 24, 23, 22, 21, 20, 15, 20]\n",
      "action_history: [1, 1, 3, 2, 2, 2, 2, 1, 1, 3, 3, 3, 3, 0, 1]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0), (0, 4, 3, 0), (0, 4, 4, 0)]\n",
      "----------\n",
      "trajectory: [0, 5, 10, 11, 12, 13, 8, 9, 14, 19, 24, 23, 22, 21, 16]\n",
      "action_history: [1, 1, 2, 2, 2, 0, 2, 1, 1, 1, 3, 3, 3, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 3, 1)]\n",
      "----------\n",
      "trajectory: [0, 5, 5, 10, 11, 12, 17, 22, 17, 22, 17, 18]\n",
      "action_history: [1, 3, 1, 2, 2, 1, 1, 0, 1, 0, 2]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 3, 2), (0, 4, 4, 2), (0, 4, 3, 2), (0, 4, 4, 2), (0, 4, 3, 2), (0, 4, 3, 3)]\n",
      "----------\n",
      "trajectory: [0, 5, 0, 1, 2, 3, 4, 4, 4, 9, 14, 19, 24, 23, 22, 21]\n",
      "action_history: [1, 0, 2, 2, 2, 2, 2, 0, 1, 1, 1, 1, 3, 3, 3]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 0, 0), (0, 4, 0, 1), (0, 4, 0, 2), (0, 4, 0, 3), (0, 4, 0, 4), (0, 4, 0, 4), (0, 4, 0, 4), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1)]\n",
      "----------\n",
      "trajectory: [0, 5, 10, 11, 12, 13, 14, 19, 24, 23, 22, 17, 12, 13, 8, 9]\n",
      "action_history: [1, 1, 2, 2, 2, 2, 1, 1, 3, 3, 0, 0, 2, 0, 2]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 3, 2), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 1, 3), (0, 4, 1, 4)]\n",
      "----------\n",
      "trajectory: [0, 5, 10, 11, 12, 13, 14, 19, 24, 23, 22, 21, 20, 21, 22, 17]\n",
      "action_history: [1, 1, 2, 2, 2, 2, 1, 1, 3, 3, 3, 3, 2, 2, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0), (0, 4, 4, 1), (0, 4, 4, 2), (0, 4, 3, 2)]\n",
      "----------\n",
      "trajectory: [0, 5, 0, 1, 2, 3, 8, 9, 14, 19, 24, 23, 22, 21, 20, 21]\n",
      "action_history: [1, 0, 2, 2, 2, 1, 2, 1, 1, 1, 3, 3, 3, 3, 2]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 0, 0), (0, 4, 0, 1), (0, 4, 0, 2), (0, 4, 0, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0), (0, 4, 4, 1)]\n",
      "----------\n",
      "trajectory: [0, 5, 10, 11, 12, 13, 14, 19, 24, 23, 22, 17, 22, 21, 20, 20]\n",
      "action_history: [1, 1, 2, 2, 2, 2, 1, 1, 3, 3, 0, 1, 3, 3, 1]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 3, 2), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0), (0, 4, 4, 0)]\n",
      "----------\n",
      "trajectory: [0, 5, 10, 11, 11, 12, 13, 14, 19, 24, 23, 22, 21, 20, 21, 20]\n",
      "action_history: [1, 1, 2, 0, 2, 2, 2, 1, 1, 3, 3, 3, 3, 2, 3]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0), (0, 4, 4, 1), (0, 4, 4, 0)]\n",
      "----------\n",
      "trajectory: [0, 5, 0, 1, 1, 2, 3, 8, 9, 14, 19, 24, 23, 22, 21, 20]\n",
      "action_history: [1, 0, 2, 1, 2, 2, 1, 2, 1, 1, 1, 3, 3, 3, 3]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 0, 0), (0, 4, 0, 1), (0, 4, 0, 1), (0, 4, 0, 2), (0, 4, 0, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0)]\n",
      "----------\n",
      "trajectory: [0, 5, 0, 1, 2, 3, 8, 9, 14, 19, 24, 23, 22, 21, 20, 15]\n",
      "action_history: [1, 0, 2, 2, 2, 1, 2, 1, 1, 1, 3, 3, 3, 3, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 0, 0), (0, 4, 0, 1), (0, 4, 0, 2), (0, 4, 0, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0), (0, 4, 3, 0)]\n",
      "----------\n",
      "trajectory: [0, 5, 0, 1, 2, 3, 8, 9, 14, 19, 24, 23, 22, 21, 20, 20]\n",
      "action_history: [1, 0, 2, 2, 2, 1, 2, 1, 1, 1, 3, 3, 3, 3, 3]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 0, 0), (0, 4, 0, 1), (0, 4, 0, 2), (0, 4, 0, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0), (0, 4, 4, 0)]\n",
      "----------\n",
      "trajectory: [0, 5, 0, 1, 2, 3, 8, 9, 14, 19, 24, 23, 22, 21, 20, 15]\n",
      "action_history: [1, 0, 2, 2, 2, 1, 2, 1, 1, 1, 3, 3, 3, 3, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 0, 0), (0, 4, 0, 1), (0, 4, 0, 2), (0, 4, 0, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0), (0, 4, 3, 0)]\n",
      "----------\n",
      "trajectory: [0, 5, 0, 1, 2, 3, 8, 9, 14, 19, 24, 23, 22, 21, 20, 15]\n",
      "action_history: [1, 0, 2, 2, 2, 1, 2, 1, 1, 1, 3, 3, 3, 3, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 0, 0), (0, 4, 0, 1), (0, 4, 0, 2), (0, 4, 0, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0), (0, 4, 3, 0)]\n",
      "----------\n",
      "trajectory: [0, 5, 0, 1, 2, 3, 8, 9, 14, 19, 24, 23, 22, 21, 20, 21]\n",
      "action_history: [1, 0, 2, 2, 2, 1, 2, 1, 1, 1, 3, 3, 3, 3, 2]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 0, 0), (0, 4, 0, 1), (0, 4, 0, 2), (0, 4, 0, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0), (0, 4, 4, 1)]\n",
      "----------\n",
      "trajectory: [0, 5, 10, 11, 12, 13, 8, 9, 14, 19, 24, 23, 22, 21, 20, 21]\n",
      "action_history: [1, 1, 2, 2, 2, 0, 2, 1, 1, 1, 3, 3, 3, 3, 2]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0), (0, 4, 4, 1)]\n",
      "----------\n",
      "trajectory: [0, 5, 0, 1, 2, 3, 8, 9, 14, 19, 24, 23, 22, 17, 12, 13]\n",
      "action_history: [1, 0, 2, 2, 2, 1, 2, 1, 1, 1, 3, 3, 0, 0, 2]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 0, 0), (0, 4, 0, 1), (0, 4, 0, 2), (0, 4, 0, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 3, 2), (0, 4, 2, 2), (0, 4, 2, 3)]\n",
      "----------\n",
      "Wins: 0 / 20\n",
      "last reward: 0.0   | trajectory: [0, 5, 0, 1, 2, 3, 8, 9, 14, 19, 24, 23, 22, 17, 12, 13]\n",
      "Actions: [1, 0, 2, 2, 2, 1, 2, 1, 1, 1, 3, 3, 0, 0, 2]\n",
      "trajectory: [0, 0, 5, 10, 11, 12, 13, 14, 19, 24, 24, 23, 22, 21, 20, 15]\n",
      "action_history: [0, 1, 1, 2, 2, 2, 2, 1, 1, 2, 3, 3, 3, 3, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0), (0, 4, 3, 0)]\n",
      "----------\n",
      "trajectory: [0, 5, 0, 1, 1, 1, 2, 3, 8, 9, 14, 19, 24, 23, 22, 21]\n",
      "action_history: [1, 0, 2, 1, 1, 2, 2, 1, 2, 1, 1, 1, 3, 3, 3]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 0, 0), (0, 4, 0, 1), (0, 4, 0, 1), (0, 4, 0, 1), (0, 4, 0, 2), (0, 4, 0, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1)]\n",
      "----------\n",
      "trajectory: [0, 5, 5, 0, 1, 2, 3, 8, 9, 14, 9, 4, 3, 8, 9, 14]\n",
      "action_history: [1, 3, 0, 2, 2, 2, 1, 2, 1, 0, 0, 3, 1, 2, 1]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 1, 0), (0, 4, 0, 0), (0, 4, 0, 1), (0, 4, 0, 2), (0, 4, 0, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 1, 4), (0, 4, 0, 4), (0, 4, 0, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4)]\n",
      "----------\n",
      "trajectory: [0, 5, 10, 11, 12, 13, 8, 9, 14, 19, 24, 23, 22, 21, 20, 21]\n",
      "action_history: [1, 1, 2, 2, 2, 0, 2, 1, 1, 1, 3, 3, 3, 3, 2]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0), (0, 4, 4, 1)]\n",
      "----------\n",
      "trajectory: [0, 5, 10, 11, 12, 13, 14, 19, 24, 23, 22, 21, 22, 17, 12, 13]\n",
      "action_history: [1, 1, 2, 2, 2, 2, 1, 1, 3, 3, 3, 2, 0, 0, 2]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 2), (0, 4, 3, 2), (0, 4, 2, 2), (0, 4, 2, 3)]\n",
      "----------\n",
      "trajectory: [0, 0, 5, 10, 11, 12, 13, 8, 9, 14, 19, 24, 23, 22, 21, 20]\n",
      "action_history: [0, 1, 1, 2, 2, 2, 0, 2, 1, 1, 1, 3, 3, 3, 3]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0)]\n",
      "----------\n",
      "trajectory: [0, 5, 10, 11, 12, 13, 14, 19, 24, 23, 22, 21, 20, 21, 20, 15]\n",
      "action_history: [1, 1, 2, 2, 2, 2, 1, 1, 3, 3, 3, 3, 2, 3, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0), (0, 4, 4, 1), (0, 4, 4, 0), (0, 4, 3, 0)]\n",
      "----------\n",
      "trajectory: [0, 5, 10, 11, 12, 13, 8, 9, 14, 19, 24, 23, 22, 17, 18]\n",
      "action_history: [1, 1, 2, 2, 2, 0, 2, 1, 1, 1, 3, 3, 0, 2]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 3, 2), (0, 4, 3, 3)]\n",
      "----------\n",
      "trajectory: [0, 5, 10, 11, 12, 13, 14, 9, 14, 19, 24, 23, 22, 17, 22, 21]\n",
      "action_history: [1, 1, 2, 2, 2, 2, 0, 1, 1, 1, 3, 3, 0, 1, 3]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 2, 4), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 3, 2), (0, 4, 4, 2), (0, 4, 4, 1)]\n",
      "----------\n",
      "trajectory: [0, 5, 10, 11, 12, 13, 14, 19, 24, 23, 22, 21, 20, 15, 20, 21]\n",
      "action_history: [1, 1, 2, 2, 2, 2, 1, 1, 3, 3, 3, 3, 0, 1, 2]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0), (0, 4, 3, 0), (0, 4, 4, 0), (0, 4, 4, 1)]\n",
      "----------\n",
      "trajectory: [0, 5, 10, 11, 12, 13, 14, 19, 24, 23, 22, 17, 12, 13, 14, 19]\n",
      "action_history: [1, 1, 2, 2, 2, 2, 1, 1, 3, 3, 0, 0, 2, 2, 1]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 3, 2), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 2, 4), (0, 4, 3, 4)]\n",
      "----------\n",
      "trajectory: [0, 5, 10, 11, 12, 13, 8, 9, 14, 19, 24, 23, 22, 21, 20, 15]\n",
      "action_history: [1, 1, 2, 2, 2, 0, 2, 1, 1, 1, 3, 3, 3, 3, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0), (0, 4, 3, 0)]\n",
      "----------\n",
      "trajectory: [0, 5, 10, 11, 12, 13, 14, 19, 24, 23, 22, 21, 20, 15, 10, 11]\n",
      "action_history: [1, 1, 2, 2, 2, 2, 1, 1, 3, 3, 3, 3, 0, 0, 2]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0), (0, 4, 3, 0), (0, 4, 2, 0), (0, 4, 2, 1)]\n",
      "----------\n",
      "trajectory: [0, 5, 10, 11, 12, 13, 8, 9, 14, 19, 24, 23, 22, 21, 20, 15]\n",
      "action_history: [1, 1, 2, 2, 2, 0, 2, 1, 1, 1, 3, 3, 3, 3, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0), (0, 4, 3, 0)]\n",
      "----------\n",
      "trajectory: [0, 5, 10, 11, 12, 13, 8, 9, 14, 19, 24, 23, 22, 21, 20, 21]\n",
      "action_history: [1, 1, 2, 2, 2, 0, 2, 1, 1, 1, 3, 3, 3, 3, 2]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0), (0, 4, 4, 1)]\n",
      "----------\n",
      "trajectory: [0, 5, 10, 11, 12, 13, 14, 19, 24, 23, 22, 21, 20, 21, 20, 21]\n",
      "action_history: [1, 1, 2, 2, 2, 2, 1, 1, 3, 3, 3, 3, 2, 3, 2]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0), (0, 4, 4, 1), (0, 4, 4, 0), (0, 4, 4, 1)]\n",
      "----------\n",
      "trajectory: [0, 5, 10, 11, 12, 13, 8, 9, 14, 19, 24, 23, 22, 21, 20, 15]\n",
      "action_history: [1, 1, 2, 2, 2, 0, 2, 1, 1, 1, 3, 3, 3, 3, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0), (0, 4, 3, 0)]\n",
      "----------\n",
      "trajectory: [0, 5, 10, 11, 12, 13, 14, 19, 24, 23, 22, 21, 20, 21, 20, 15]\n",
      "action_history: [1, 1, 2, 2, 2, 2, 1, 1, 3, 3, 3, 3, 2, 3, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0), (0, 4, 4, 1), (0, 4, 4, 0), (0, 4, 3, 0)]\n",
      "----------\n",
      "trajectory: [0, 5, 10, 11, 12, 13, 8, 9, 14, 19, 24, 23, 22, 21, 20, 15]\n",
      "action_history: [1, 1, 2, 2, 2, 0, 2, 1, 1, 1, 3, 3, 3, 3, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0), (0, 4, 3, 0)]\n",
      "----------\n",
      "trajectory: [0, 5, 10, 11, 12, 13, 8, 9, 4, 3, 8, 9, 14, 19, 24, 23]\n",
      "action_history: [1, 1, 2, 2, 2, 0, 2, 0, 3, 1, 2, 1, 1, 1, 3]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 0, 4), (0, 4, 0, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3)]\n",
      "----------\n",
      "Wins: 0 / 20\n",
      "last reward: 0.0   | trajectory: [0, 5, 10, 11, 12, 13, 8, 9, 4, 3, 8, 9, 14, 19, 24, 23]\n",
      "Actions: [1, 1, 2, 2, 2, 0, 2, 0, 3, 1, 2, 1, 1, 1, 3]\n",
      "trajectory: [0, 5, 10, 11, 12, 13, 8, 13, 8, 9, 14, 19, 24, 23, 22, 17]\n",
      "action_history: [1, 1, 2, 2, 2, 0, 1, 0, 2, 1, 1, 1, 3, 3, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 1, 3), (0, 4, 2, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 3, 2)]\n",
      "----------\n",
      "trajectory: [0, 5, 10, 15, 16]\n",
      "action_history: [1, 1, 1, 2]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 3, 0), (0, 4, 3, 1)]\n",
      "----------\n",
      "trajectory: [0, 5, 10, 11, 12, 13, 14, 19, 24, 23, 22, 21, 20, 21, 20, 20]\n",
      "action_history: [1, 1, 2, 2, 2, 2, 1, 1, 3, 3, 3, 3, 2, 3, 3]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0), (0, 4, 4, 1), (0, 4, 4, 0), (0, 4, 4, 0)]\n",
      "----------\n",
      "trajectory: [0, 5, 10, 11, 12, 13, 8, 9, 14, 19, 24, 23, 22, 17, 12, 13]\n",
      "action_history: [1, 1, 2, 2, 2, 0, 2, 1, 1, 1, 3, 3, 0, 0, 2]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 3, 2), (0, 4, 2, 2), (0, 4, 2, 3)]\n",
      "----------\n",
      "trajectory: [0, 5, 10, 11, 12, 13, 14, 19, 24, 23, 22, 22, 21, 20, 15, 10]\n",
      "action_history: [1, 1, 2, 2, 2, 2, 1, 1, 3, 3, 1, 3, 3, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0), (0, 4, 3, 0), (0, 4, 2, 0)]\n",
      "----------\n",
      "trajectory: [0, 5, 10, 11, 12, 13, 14, 19, 24, 23, 22, 17, 12, 13, 14, 19]\n",
      "action_history: [1, 1, 2, 2, 2, 2, 1, 1, 3, 3, 0, 0, 2, 2, 1]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 3, 2), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 2, 4), (0, 4, 3, 4)]\n",
      "----------\n",
      "trajectory: [0, 5, 10, 11, 12, 13, 8, 9, 14, 19, 24, 23, 22, 21, 20, 15]\n",
      "action_history: [1, 1, 2, 2, 2, 0, 2, 1, 1, 1, 3, 3, 3, 3, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0), (0, 4, 3, 0)]\n",
      "----------\n",
      "trajectory: [0, 5, 10, 11, 12, 13, 8, 9, 14, 19, 24, 23, 22, 21, 20, 20]\n",
      "action_history: [1, 1, 2, 2, 2, 0, 2, 1, 1, 1, 3, 3, 3, 3, 1]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0), (0, 4, 4, 0)]\n",
      "----------\n",
      "trajectory: [0, 5, 5, 0, 1, 2, 3, 8, 9, 14, 19, 24, 23, 22, 21, 20]\n",
      "action_history: [1, 3, 0, 2, 2, 2, 1, 2, 1, 1, 1, 3, 3, 3, 3]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 1, 0), (0, 4, 0, 0), (0, 4, 0, 1), (0, 4, 0, 2), (0, 4, 0, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0)]\n",
      "----------\n",
      "trajectory: [0, 5, 5, 10, 11, 12, 13, 8, 9, 14, 19, 24, 23, 22, 21, 20]\n",
      "action_history: [1, 3, 1, 2, 2, 2, 0, 2, 1, 1, 1, 3, 3, 3, 3]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0)]\n",
      "----------\n",
      "trajectory: [0, 5, 5, 0, 1, 2, 3, 8, 9, 14, 19, 24, 23, 22, 21, 20]\n",
      "action_history: [1, 3, 0, 2, 2, 2, 1, 2, 1, 1, 1, 3, 3, 3, 3]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 1, 0), (0, 4, 0, 0), (0, 4, 0, 1), (0, 4, 0, 2), (0, 4, 0, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0)]\n",
      "----------\n",
      "trajectory: [0, 5, 5, 5, 10, 11, 12, 13, 8, 9, 14, 19, 24, 23, 22, 21]\n",
      "action_history: [1, 3, 3, 1, 2, 2, 2, 0, 2, 1, 1, 1, 3, 3, 3]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 1, 0), (0, 4, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1)]\n",
      "----------\n",
      "trajectory: [0, 5, 5, 0, 1, 2, 3, 8, 9, 14, 19, 24, 23, 22, 21, 20]\n",
      "action_history: [1, 3, 0, 2, 2, 2, 1, 2, 1, 1, 1, 3, 3, 3, 3]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 1, 0), (0, 4, 0, 0), (0, 4, 0, 1), (0, 4, 0, 2), (0, 4, 0, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0)]\n",
      "----------\n",
      "trajectory: [0, 5, 5, 10, 11, 12, 13, 8, 9, 14, 19, 24, 23, 22, 21, 20]\n",
      "action_history: [1, 3, 1, 2, 2, 2, 0, 2, 1, 1, 1, 3, 3, 3, 3]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0)]\n",
      "----------\n",
      "trajectory: [0, 5, 5, 10, 11, 12, 13, 8, 9, 14, 19, 24, 23, 22, 21, 20]\n",
      "action_history: [1, 3, 1, 2, 2, 2, 0, 2, 1, 1, 1, 3, 3, 3, 3]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0)]\n",
      "----------\n",
      "trajectory: [0, 5, 5, 10, 11, 12, 13, 14, 19, 24, 23, 22, 17, 12, 13, 14]\n",
      "action_history: [1, 3, 1, 2, 2, 2, 2, 1, 1, 3, 3, 0, 0, 2, 2]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 3, 2), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 2, 4)]\n",
      "----------\n",
      "trajectory: [0, 5, 10, 11, 12, 13, 8, 9, 14, 19, 24, 23, 22, 21, 20, 15]\n",
      "action_history: [1, 1, 2, 2, 2, 0, 2, 1, 1, 1, 3, 3, 3, 3, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0), (0, 4, 3, 0)]\n",
      "----------\n",
      "trajectory: [0, 0, 5, 0, 1, 2, 3, 8, 9, 14, 19, 24, 23, 22, 21, 20]\n",
      "action_history: [3, 1, 0, 2, 2, 2, 1, 2, 1, 1, 1, 3, 3, 3, 3]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 0, 0), (0, 4, 0, 1), (0, 4, 0, 2), (0, 4, 0, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0)]\n",
      "----------\n",
      "trajectory: [0, 5, 5, 5, 0, 1, 2, 3, 8, 9, 14, 19, 24, 23, 22, 21]\n",
      "action_history: [1, 3, 3, 0, 2, 2, 2, 1, 2, 1, 1, 1, 3, 3, 3]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 1, 0), (0, 4, 1, 0), (0, 4, 0, 0), (0, 4, 0, 1), (0, 4, 0, 2), (0, 4, 0, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1)]\n",
      "----------\n",
      "trajectory: [0, 5, 0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 17, 12, 13]\n",
      "action_history: [1, 0, 2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 0, 0, 2]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 0, 0), (0, 4, 0, 1), (0, 4, 0, 2), (0, 4, 0, 3), (0, 4, 0, 4), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 3, 2), (0, 4, 2, 2), (0, 4, 2, 3)]\n",
      "----------\n",
      "Wins: 0 / 20\n",
      "last reward: 0.0   | trajectory: [0, 5, 0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 17, 12, 13]\n",
      "Actions: [1, 0, 2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 0, 0, 2]\n",
      "trajectory: [0, 5, 5, 10, 11, 12, 13, 8, 9, 14, 19, 24, 23, 22, 21, 20]\n",
      "action_history: [1, 3, 1, 2, 2, 2, 0, 2, 1, 1, 1, 3, 3, 3, 3]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0)]\n",
      "----------\n",
      "trajectory: [0, 0, 5, 5, 5, 10, 11, 12, 13, 8, 9, 14, 19, 24, 23, 22]\n",
      "action_history: [3, 1, 3, 3, 1, 2, 2, 2, 0, 2, 1, 1, 1, 3, 3]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 1, 0), (0, 4, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2)]\n",
      "----------\n",
      "trajectory: [0, 5, 5, 0, 1, 2, 3, 2, 3, 8, 9, 14, 19, 24, 23, 22]\n",
      "action_history: [1, 3, 0, 2, 2, 2, 3, 2, 1, 2, 1, 1, 1, 3, 3]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 1, 0), (0, 4, 0, 0), (0, 4, 0, 1), (0, 4, 0, 2), (0, 4, 0, 3), (0, 4, 0, 2), (0, 4, 0, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2)]\n",
      "----------\n",
      "trajectory: [0, 5, 5, 10, 11, 12, 13, 14, 19, 24, 23, 22, 21, 20, 20, 15]\n",
      "action_history: [1, 3, 1, 2, 2, 2, 2, 1, 1, 3, 3, 3, 3, 1, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0), (0, 4, 4, 0), (0, 4, 3, 0)]\n",
      "----------\n",
      "trajectory: [0, 5, 5, 10, 11, 11, 12, 13, 14, 19, 24, 23, 22, 21, 20, 21]\n",
      "action_history: [1, 3, 1, 2, 0, 2, 2, 2, 1, 1, 3, 3, 3, 3, 2]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0), (0, 4, 4, 1)]\n",
      "----------\n",
      "trajectory: [0, 5, 5, 10, 11, 12, 13, 8, 9, 14, 19, 24, 23, 22, 17, 12]\n",
      "action_history: [1, 2, 1, 2, 2, 2, 0, 2, 1, 1, 1, 3, 3, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 3, 2), (0, 4, 2, 2)]\n",
      "----------\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 8, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 1, 2, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 1, 3), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "trajectory: [0, 5, 0, 1, 2, 3, 8, 9, 14, 19, 24, 23, 22, 17, 22, 21]\n",
      "action_history: [1, 0, 2, 2, 2, 1, 2, 1, 1, 1, 3, 3, 0, 1, 3]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 0, 0), (0, 4, 0, 1), (0, 4, 0, 2), (0, 4, 0, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 3, 2), (0, 4, 4, 2), (0, 4, 4, 1)]\n",
      "----------\n",
      "success ep: 3 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "trajectory: [0, 1, 1, 1, 0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21]\n",
      "action_history: [2, 1, 1, 3, 2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 1), (0, 0, 0, 1), (0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1)]\n",
      "----------\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "trajectory: [0, 5, 5, 10, 11, 12, 13, 8, 9, 14, 19, 24, 23, 22, 21, 22]\n",
      "action_history: [1, 2, 1, 2, 2, 2, 0, 2, 1, 1, 1, 3, 3, 3, 2]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 2)]\n",
      "----------\n",
      "trajectory: [0, 5, 5, 10, 11, 12, 13, 14, 19, 24, 23, 22, 21, 20, 20, 21]\n",
      "action_history: [1, 2, 1, 2, 2, 2, 2, 1, 1, 3, 3, 3, 3, 3, 2]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1), (0, 4, 4, 0), (0, 4, 4, 0), (0, 4, 4, 1)]\n",
      "----------\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 8, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 1, 2, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 1, 3), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "trajectory: [0, 5, 0, 1, 2, 3, 8, 9, 14, 19, 24, 23, 22, 23, 22, 21]\n",
      "action_history: [1, 0, 2, 2, 2, 1, 2, 1, 1, 1, 3, 3, 2, 3, 3]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 0, 0), (0, 4, 0, 1), (0, 4, 0, 2), (0, 4, 0, 3), (0, 4, 1, 3), (0, 4, 1, 4), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 4, 1)]\n",
      "----------\n",
      "success ep: 2 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 2 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "Wins: 9 / 20\n",
      "last reward: 0.0   | trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "Actions: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "trajectory: [0, 5, 5, 10, 11, 12, 13, 14, 19, 24, 23, 23, 22, 17, 22, 23]\n",
      "action_history: [1, 3, 1, 2, 2, 2, 2, 1, 1, 3, 1, 3, 0, 1, 2]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 1, 0), (0, 4, 1, 0), (0, 4, 2, 0), (0, 4, 2, 1), (0, 4, 2, 2), (0, 4, 2, 3), (0, 4, 2, 4), (0, 4, 3, 4), (0, 4, 4, 4), (0, 4, 4, 3), (0, 4, 4, 3), (0, 4, 4, 2), (0, 4, 3, 2), (0, 4, 4, 2), (0, 4, 4, 3)]\n",
      "----------\n",
      "success ep: 2 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 3 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 3 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 2 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "Wins: 19 / 20\n",
      "last reward: 0.0   | trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "Actions: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 3 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "Wins: 20 / 20\n",
      "last reward: 0.0   | trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "Actions: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "Wins: 20 / 20\n",
      "last reward: 0.0   | trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "Actions: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "success ep: 2 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 2 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 2 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "Wins: 20 / 20\n",
      "last reward: 0.0   | trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "Actions: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "success ep: 1 / 3\n",
      "states (if in acc) [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n",
      "trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "action_history: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n",
      "state history: [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 0, 2), (0, 0, 0, 3), (0, 0, 0, 4), (0, 0, 1, 4), (0, 0, 2, 4), (0, 1, 3, 4), (0, 1, 4, 4), (0, 1, 4, 3), (0, 1, 4, 2), (0, 1, 4, 1), (0, 2, 4, 0), (0, 2, 3, 0), (0, 2, 2, 0), (0, 2, 1, 0)]\n",
      "----------\n",
      "[True, True, True, True, True, True, True, False, False, False, False, False, False, False, False, False]\n",
      "Wins: 20 / 20\n",
      "last reward: 0.0   | trajectory: [0, 1, 2, 3, 4, 9, 14, 19, 24, 23, 22, 21, 20, 15, 10, 5]\n",
      "Actions: [2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 0, 0, 0]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "num_epochs = 20\n",
    "training = True\n",
    "T = 16\n",
    "results = []\n",
    "search_depths = [2,4,8,16,32,40,60,80,100,120,200] #[2,4,6,8,10,15,20,25,30,35,40,45,50,60,70,80,90,100,120]\n",
    "for sd in search_depths:\n",
    "    wins = 0\n",
    "    N, W, Q, P = np.zeros(csrl.shape), np.zeros(csrl.shape), np.zeros(csrl.shape), np.zeros(csrl.shape)\n",
    "    for epoch in range(num_epochs):\n",
    "        state_history, channeled_states, trajectory, action_history, reward_history, better_policy = csrl.MC_learning(model, LTL_formula, predicates,\n",
    "                N = N, W = W, Q = Q, P = P, C=3, tow=1, n_samples=sd,\n",
    "                verbose=4,start=(0,0),T=T,K=3)\n",
    "        if check_LTL(LTL_formula, trajectory, predicates)[0]:\n",
    "            wins+=1\n",
    "            print(check_LTL(LTL_formula, trajectory, predicates))\n",
    "\n",
    "        if training:\n",
    "            x_train = np.array(channeled_states)\n",
    "            y1_train = np.array(better_policy)\n",
    "            y2_train = np.array(reward_history) + (check_LTL(LTL_formula, trajectory, predicates)[0]*max_rew)\n",
    "            model.fit(x_train[:-1], [y1_train, y2_train[:-1]], epochs=20, verbose=0)\n",
    "\n",
    "    ###############################################################\n",
    "    print(\"Wins:\",wins,\"/\",num_epochs)\n",
    "    print(\"last reward:\", reward_history[-1], \"  | trajectory:\", trajectory)\n",
    "    print(\"Actions:\", action_history)\n",
    "    results.append(wins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(search_depths, [r*5 for r in results])\n",
    "plt.xlabel(\"number of MCTS samples in each time-step\")\n",
    "plt.ylabel(\"succes rate\")\n",
    "plt.title(\"Succes rate, over 20 runs for each MCTS sample size\")\n",
    "plt.savefig('line_plot2.pdf') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ipywidgets.widgets import IntSlider\n",
    "from ipywidgets import interact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "91214c8703d9403cb4618fc801f74828",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=0, description='t', max=15), Output()), _dom_classes=('widget-interact',…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.plot_agent(t)>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def plot_agent(t):\n",
    "    csrl.mdp.plot(agent=state_history[t][2:], save=f'test/{t}.png')\n",
    "t=IntSlider(value=0,min=0,max=T-1)\n",
    "interact(plot_agent,t=t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.system('ffmpeg -r 3 -i ./test/%d.png -vcodec libx264 -y ./test/test.mp4')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.16091954, 0.97701149, 0.98850575, 0.91954023,\n",
       "       0.97701149, 0.95402299, 0.97701149])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([0, 14, 85, 86, 80, 85, 83, 85])/87"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.551470164434615\n",
      "2.179449471770337\n",
      "2.7129319932501073\n",
      "1.9595917942265424\n",
      "2.179449471770337\n"
     ]
    }
   ],
   "source": [
    "# SP4LTL\n",
    "T = [25, 50, 100, 200, 400]\n",
    "success_rates= [93, 95, 92, 96, 95]\n",
    "\n",
    "succes_std = []\n",
    "for i in success_rates:\n",
    "    temp = np.zeros(100)\n",
    "    temp[:int(i)]=1\n",
    "    std = np.sqrt(100*np.var(temp))\n",
    "    print(std)\n",
    "    succes_std.append((i-std, i+std))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([51.        , 56.        , 56.        , 49.33333333, 56.33333333])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = [\n",
    "    [52, 65, 55, 50, 49],\n",
    "    [53, 48, 59, 51, 64],\n",
    "    [48, 55, 54, 47, 56]\n",
    "]\n",
    "\n",
    "np.mean(x, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[97, 100, 98, 99, 100, 99, 99, 100]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "succes_std = np.array(succes_std)\n",
    "\n",
    "plt.figure(figsize=(7,4))\n",
    "plt.fill_between(T, succes_std[:,0], succes_std[:,1], alpha=0.3)\n",
    "\n",
    "plt.plot(T, success_rates, label='old approach')\n",
    "plt.xlabel(\"length of trajectory (T)\")\n",
    "plt.ylabel(f\"succes rate\")\n",
    "# plt.title(\"Self-play results on the third case; stochastic\")\n",
    "plt.title(\"Self-play results on the third case; deterministic\")\n",
    "plt.ylim(-10, 110)\n",
    "plt.xlim(0, 410)\n",
    "plt.savefig(f\"res_400/C3_P{p}.pdf\")\n",
    "plt.legend()\n",
    "success_rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.705872210923198\n",
      "0.0\n",
      "2.179449471770337\n",
      "1.9595917942265424\n",
      "2.7129319932501073\n"
     ]
    }
   ],
   "source": [
    "# CSRL\n",
    "T2 = [25, 50, 100, 200, 400]\n",
    "success_rates2= [97, 100, 95, 96, 92]\n",
    "succes_std2 = []\n",
    "for i in success_rates2:\n",
    "    temp = np.zeros(100)\n",
    "    temp[:int(i)]=1\n",
    "    std = np.sqrt(100*np.var(temp))\n",
    "    print(std)\n",
    "    succes_std2.append((i-std, i+std))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.74 0.4386342439892262\n",
      "6.6 0.4898979485566356\n",
      "15.0 0.0\n",
      "32.96 0.19595917942265423\n"
     ]
    }
   ],
   "source": [
    "a = [[4 for i in range(37)] + [3 for i in range(13)],\n",
    "     [7 for i in range(30)] + [6 for i in range(20)],\n",
    "     [15 for i in range(50)],\n",
    "     [33 for i in range(48)] + [32 for i in range(2)]]\n",
    "    \n",
    "for i in np.array(a):\n",
    "    print((i[i>0]).mean(), np.sqrt(np.var((i[i>0]))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "p=1\n",
    "case=3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[93, 95, 92, 96, 92, 95, 95, 98]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "succes_std = np.array(succes_std)\n",
    "succes_std2 = np.array(succes_std2)\n",
    "\n",
    "plt.figure(figsize=(7,4))\n",
    "plt.fill_between(T, succes_std[:,0], succes_std[:,1], alpha=0.3)\n",
    "plt.fill_between(T2, succes_std2[:,0], succes_std2[:,1], alpha=0.3)\n",
    "\n",
    "plt.plot(T, success_rates, label='us')\n",
    "plt.plot(T2, success_rates2, label='csrl')\n",
    "plt.xlabel(\"length of trajectory (T)\")\n",
    "plt.ylabel(f\"succes rate\")\n",
    "# plt.title(\"Success rates on the nursery case; stochastic\")\n",
    "# plt.title(\"Success rates on the safe absorbing case; deterministic\")\n",
    "plt.ylim(0, 110)\n",
    "plt.xlim(0, 410)\n",
    "plt.legend()\n",
    "# plt.savefig(f\"results/C{case}_comp_p={p}.pdf\")\n",
    "# plt.savefig(f\"results/C{case}_comp_p={p}.pdf\")\n",
    "success_rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "T = [25, 50, 100, 150, 200, 300, 400]\n",
    "acc_rews = [0.01, 0.04, 0.07, 0.12, 0.16, 0.21, 0.32]\n",
    "\n",
    "plt.figure(figsize=(7,4))\n",
    "plt.plot(T, acc_rews, label='us')\n",
    "plt.plot(T, csrl_acc_rews, label='csrl')\n",
    "plt.ylim(0, 110)\n",
    "plt.xlim(0, 410)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pylab as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pc-admin\\AppData\\Local\\Temp\\ipykernel_21148\\2869943138.py:1: MatplotlibDeprecationWarning: The seaborn styles shipped by Matplotlib are deprecated since 3.6, as they no longer correspond to the styles shipped by seaborn. However, they will remain available as 'seaborn-v0_8-<style>'. Alternatively, directly use the seaborn API instead.\n",
      "  plt.style.use(\"seaborn\")\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 400x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 400x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 400x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use(\"seaborn\")\n",
    " \n",
    "# 2. Generate a 10x10 random integer matrix\n",
    "data = [\n",
    "    [51, 79, 86, 45],\n",
    "    [73, 93, 93, 94],\n",
    "    [77, 90, 85, 94],\n",
    "    [81, 90, 88, 95]\n",
    "]\n",
    " \n",
    "# 3. Plot the heatmap\n",
    "plt.figure(figsize=(4,4))\n",
    "heat_map = sns.heatmap( data, linewidth = 1 , annot = True, xticklabels=[25, 50, 75, 100], yticklabels=['d 25', 'd 50', 'd 75', 'd 100'])\n",
    "plt.title(\"varying depth search succes rate\")\n",
    "plt.show()\n",
    "\n",
    "data = [\n",
    "    [2, 48, 47, 49],\n",
    "    [83, 90, 90, 95],\n",
    "    [51, 95, 93, 93],\n",
    "    [75, 90, 95, 90]\n",
    "]\n",
    " \n",
    "# 3. Plot the heatmap\n",
    "plt.figure(figsize=(4,4))\n",
    "heat_map = sns.heatmap( data, linewidth = 1 , annot = True, xticklabels=[25, 50, 75, 100], yticklabels=['s 50','s 200', 's 500', 's 1000'])\n",
    "plt.title(\"varying # MCTS sampling succes rate\")\n",
    "plt.show()\n",
    "\n",
    "data = [\n",
    "    [30, 50, 50, 55],\n",
    "    [72, 92, 92, 91],\n",
    "    [93, 88, 93, 92],\n",
    "    [87, 92, 90, 91],\n",
    "]\n",
    " \n",
    "# 3. Plot the heatmap\n",
    "plt.figure(figsize=(4,4))\n",
    "heat_map = sns.heatmap( data, linewidth = 1 , annot = True, xticklabels=[25, 50, 75, 100], yticklabels=['t 10', 't 50', 't 100', 't ada'])\n",
    "plt.title(\"varying training runs succes rate\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pc-admin\\AppData\\Local\\Temp\\ipykernel_21148\\908381384.py:1: MatplotlibDeprecationWarning: The seaborn styles shipped by Matplotlib are deprecated since 3.6, as they no longer correspond to the styles shipped by seaborn. However, they will remain available as 'seaborn-v0_8-<style>'. Alternatively, directly use the seaborn API instead.\n",
      "  plt.style.use(\"seaborn\")\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 400x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVkAAAFyCAYAAABfmakuAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8o6BhiAAAACXBIWXMAAA9hAAAPYQGoP6dpAABKeklEQVR4nO3de1xM+f8H8Nd001S6KeXSVy7VEilRKnfS4osQ2qV1a4Wy+CpyWSySy9IKhZKWWqJ1WTa3vih21ZKQy1C+dl3KJek+NU1zfn/sz6xZt2lnzsw0834+HvN4zHzOmc+8Z8707nPe53POcBiGYUAIIYQVWsoOgBBC1BklWUIIYRElWUIIYRElWUIIYRElWUIIYRElWUIIYRElWUIIYRElWUIIYRElWUKIwmjiuU+NOslmZ2fDwcEB2dnZCnvNU6dOoXfv3uLH69atw+zZs9+7/qFDh+Dg4AAHBwc8ePDgnetkZmaK1/m7Z8+eYf369fj000/RtWtX9OrVC0FBQbh8+bJ4ndefw8dub35Ohw4dgr+/P7p164auXbti2LBhiIqKQmVl5T/5WFRKQEAAAgICxI8dHBywZcsWJUZEAODgwYNYt26dssNQOB1lByALR0dHpKSkoEOHDgp7zZycHLi6uoofX716FUOGDPno87S0tHDixAnMmjXrrWVpaWnvfa3g4GCYmZnhiy++QNu2bVFWVoYDBw4gICAAq1evhp+fn/hzeO3WrVtYuXIlli1bBkdHR3H7689p69at2L59OyZPnoyZM2dCV1cXN2/eRHx8PC5evIj9+/dDV1dX6s9E1aWkpMDa2lrZYWi82NhYuLm5KTsMhWvUSdbIyAjOzs4Kfc2rV69i5MiRAIDa2lrcunULS5Ys+ejzunXr9s4kKxAIkJ6ejo4dO+LOnTvi9tLSUsydOxe2trbYvXs3uFyueNngwYMxa9YsfPPNN+jTpw+aN28u8TnU1tYC+DOp/v3zEQgEiIuLw9SpU/Gf//xH3O7p6Yl27dohODgY6enpUv3jaCwU/R0h5E0ylQu+/vpr9OzZE0KhUKJ9w4YNcHNzg0AgAACkp6fj888/h4uLCzp37oxPP/0USUlJ4vVf7+7u378f/fv3h6enJ9LT0+Hg4ICLFy9K9H3t2jU4ODjgt99+e6tcsGXLFnh7e+P8+fMYPnw4OnfuDB8fHxw+fFiij/v37+PLL79Et27d4OnpiaioKCxatEhiF/NNb+7y5+XlYfXq1XBwcICTkxPq6uowduzYj+6ODh06FPfu3cP9+/cl2jMzM8HhcNCnTx+J9iNHjuD58+dYvHixRIIF/hwVz58/HxMmTGjw7n1lZSVqamreWRvr27cv5s2bBxsbm/c+v7a2VpzcX2/LhIQEiXV4PB5CQkLQs2dPODo6onfv3li9ejVqamrE6zg4OGDfvn0IDw+Hq6sr3NzcxOusW7cOPXv2hLu7O5YsWSL+p/H6eUlJSVi4cCFcXFzg6en5Vt9/92a54PV35tKlS5g6dSq6du0KT09PrFu3TuJ7XFlZiWXLlsHDwwMuLi6YN28eEhMT31nSeVNaWhpGjBgBJycn9OzZE6GhoXj+/Ll4+YABAxAeHi7xnNffr8ePH4vbbt68icDAQLi6uqJnz56YN28eioqKxMtfvnyJxYsXw9PTEy4uLpgwYQJycnLEy0UiEXbu3Alvb2/x38HevXslXvfRo0eYOXMm3N3d0bVrV4wfPx4ZGRkffH8DBgzAmjVrMGnSJHTr1g3Lli0D8PFtPmDAADx58gSHDx+WeK+FhYX4z3/+Azc3N3Tt2hWTJk3C7du3PxhDYyNTkh05ciRevXqFS5cuidsYhkFaWho+/fRT6Onp4fz58wgODoajoyNiYmKwZcsWtGrVCqtWrcLVq1cl+ouKisLChQuxcOFCuLu7w8rKCkePHpVY5/Dhw7CxsUGPHj3eGdOLFy+wcuVKfPHFF9i5cydat26N8PBwcXIrKSnBxIkTUVRUhMjISCxduhQnT57E8ePH3/s++/Xrh5SUFKxYsQK6urrYt28fUlJSMGHCBHTs2BEpKSkYO3bsBz8rLy8vmJiY4MSJExLtaWlp8Pb2fmv3/MKFC2jWrBmcnJze2Z+dnR3Cw8PRrl27D77u35mbm6Nr167YtWsXFi5ciPT0dJSUlAAAdHV1MWPGDHTu3Pm9z4+IiEBGRgYWLlyIXbt2YeDAgVi3bh0OHToEAHj+/DkmTJgAPp+PtWvXIi4uDkOGDMHevXuRmJgo0de3334LPT09bN26FSNHjsTevXvh6+uLoqIibNiwAf7+/khNTX0rOWzevBkvX77Ed999h8DAQBw4cABhYWEN+hxCQ0Ph6uqK7du3Y/jw4UhISEBqaqp4eXBwME6cOIHZs2cjKioKVVVV2Lhx4wf7zMnJQWhoKAYPHoy4uDgsWrQIWVlZmD9/foNi4/F4+Oyzz8Sf4cqVK3H79m1MnToVdXV1qK6uhr+/P3799VfMnz8fW7duhaGhIQIDA8Xf8xUrViA6OhojRozA9u3b8emnn2LNmjXYtm0bgD+TcFBQEKqrq7F+/XrExMTA1NQUs2bNwh9//PHB+JKTk8X/uEaOHCnVNt+6dSssLS3Rt29fpKSkoHnz5igpKYG/vz9u3bqFr7/+Ghs3boRIJMKECRPeGow0ZjKVC1xdXdG6dWukpaWJDwbl5OSgsLBQvEtdUFAAX19fiV1qFxcXuLu74/Lly+jWrZu43d/fH59++qn4sa+vL/bu3YuqqioYGhpCIBDgxIkTmDRpEjgczjtj4vP5iIiIgIeHBwDA1tYW/fv3R0ZGBtq3by/u78iRI7CysgIAdO3aFT4+Pu99n+bm5jA3N8eNGzfQsWNHccyJiYlwc3OTandUR0cHgwYNwqlTpxASEiKO9dy5c9i2bZvEKAT484BX69atP9rvPxEdHY2wsDAcOXIER44cAYfDgZ2dHQYNGoTJkyfDxMTkvc/97bff4OnpiWHDhgEA3N3dYWBgADMzMwDAvXv30LFjR2zevBlGRkYA/ixFXLp0CZcvX8aMGTPEfbVv3x4rV64EAPTo0QOpqamoq6vDt99+Cx0dHfTu3Rtnz55965+xubk5tm/fDh0dHfTt2xdaWlqIjIxEfn4+7OzspPoMxo4di+DgYACAh4cH0tPTcf78efj7++PSpUvIysrCli1bMHjwYABAnz59MHz4cBQUFLy3z5ycHDRp0gRffvklmjRpAgAwNTVFXl4eGIZ573f272JiYmBiYoKEhARxP9bW1pg7dy7u3r2L69ev49GjRzhy5Ag++eQTAED37t3h6+uLy5cvQ0tLCwcOHMB//vMfTJ8+HQDQq1cvcDgc7NixA59//jmEQiHu37+PGTNmoG/fvgAAJycnbN26VWLP4V2aN2+O8PBwaGn9OUa7ePHiR7d5p06doKenB3Nzc/Hfy/fff4/S0lLs27cPrVq1En/OQ4cOxebNmxEdHS3V56XqZBrJcjgcjBgxAmfOnBGXBo4fPw4bGxvxwaHAwECsW7cO1dXV4PF4OHHiBHbu3AkAqKurk+jv77tiY8aMAZ/Px5kzZwD8WXYoLy+Hr6/vB+N6M+m9PuBRXV0NAMjKyoKLi4s4wQJAq1at4OLi8t7+GIaBUCjE9evX0aVLFwiFQgiFQty4cUP8WCQSfTAm4O2Swblz52BgYAB3d/e31uVwOKivr/9on/+EtbU19u7di59//hkLFy5E37598eTJE8TExGDo0KH4/fff3/tcd3d3HDx4EF9++SV++OEHPHnyBMHBwejfvz+AP/+Yk5KS0KRJEzx48ADnzp3D9u3bUVJSIv6OvPbmZ66jowMzMzN07twZOjp//e83NTVFRUWFxPOGDRsmsc7rf5BXrlyR+jP4+/a2traW+I7o6upi0KBB4uVaWlofrVP36NEDNTU1GD58OKKiopCTk4NevXohJCRE6gQL/Jms+/TpI06wwJ8J8OzZs+jcuTOuXLmC1q1bixMsADRp0gQnTpyAv78/srKywDAMBgwYIP6uCoVCDBgwALW1tcjJyYGFhQU6dOiAr7/+GuHh4UhLSwPDMFi0aBHs7e0/GF/79u3FCRZo2DZ/06VLl9CxY0dYWVmJY9TS0kKfPn3w66+/Sv15qTqZD3z5+voiJiYGmZmZ6NevH06ePInPP/9cvLykpATLly9Heno6OBwO2rRpI07Af68LNmvWTOJxmzZt0KNHDxw5cgS+vr44cuQIevbsKf6v9z5v1jBffxlev1ZJSYnEEffXLC0t8eLFi3f2d/jwYSxatEj8ODk5WXw/NDQUoaGhCAkJ+eBULgDo2bMnzMzMcOLECYSEhIjLKtra2m+t26pVK9y4ceOD/RUVFaFFixYfXOdDOnTogA4dOoh3Qw8dOoSVK1di06ZN7x1FLFmyBNbW1vjpp5/wzTffAPgzYS1btgydOnWCSCTCpk2bkJycjOrqarRo0QJOTk4SCeO116OeN/29/vwuzZs3l3j8+ntTXl7+0ee+pq+vL/FYS0tL/B159eoVTE1NJRIJAFhYWHywTxcXF+zcuROJiYnYtWsXtm/fDktLS3z55ZeYNGmS1LGVlpa+9bfQ0OUAxHsbf/fs2TNwOBwkJCQgNjYWZ86cweHDh8X/WFasWAFTU9P39v/3z6Eh2/zvcf7xxx/v/HsE/tzTk+b7oOpkTrJt2rSBs7MzTpw4AV1dXbx69QojRowQLw8NDcX9+/exe/dudOvWDXp6euDz+Th48KBU/Y8ZMwaLFi3CgwcP8MsvvyAyMlKmeK2trfHy5cu32t/V9lr//v2RmJiIyZMnY+vWrbC2tsaVK1ewbds27N69G8Dbf/jvoqOjg8GDB+PkyZOYPHkyMjMz36pTvta7d2+cO3cOeXl56NKly1vL8/Pz8e9//xvz588X7xJK4/vvv0dsbCzOnTsn8QXW1dUVH/j40C6xnp4eZs6ciZkzZ6KwsBDnzp1DTEwM5s+fL95LSUxMxIoVK+Dj44OmTZsCAPz8/KSO8WNeJ5HXiouLAfxZRpAHKysrvHr1CiKRSCLRfug78lrv3r3Ru3dv8Pl8ZGVlYc+ePVizZg2cnZ3RtWtXAHhrD+X1CPq1pk2biuvkb8rIyMAnn3yCpk2bShwkey03NxdGRkYwNjYG8Oe2NjQ0fGu9li1bit/nihUrsHz5cvB4PJw8eRJxcXEwMTER/wOVxj/d5k2bNoWbmxsWLFjwzuV6enpSx6DK5HIywogRI5CZmYnjx4/D2dkZtra24mU5OTnw8fFBz549xR9aZmYmAEi1i+3j4wMDAwMsW7YM+vr64hrZP9WjRw/k5uZKjFpfvHiBa9euvfc5ZmZm0NHRgb6+PgYMGIAuXbqgrq4ODg4O6NKlC7p06SJRfviQoUOHIj8/H/Hx8bCwsHhvmWLEiBGwtLTEmjVrwOfzJZaJRCJs2LABurq67x2tvE+HDh3w6tWrtw4mAX/+8T969Oi9u4s1NTXw8fERzyZo2bIlJkyYgGHDhuHp06cA/tzeHTp0gJ+fn/iP7dmzZ7h3755U21saZ8+elXh86tQpcDgc9OzZUy79u7m5QSgUvvU66enpH3zeunXr4OfnB4ZhwOVy0b9/fyxcuBAAxDMDjIyMxJ/Va3+vOXfv3h0XLlyQ2NW+e/cupk+fjry8PHTv3h2PHj3C3bt3xcsFAgFmz56NAwcOiA8Kv3r1Svz97NKlC0pLS/Hdd9+htLQUubm58PT0xI0bN8DhcNCxY0fMmzcP9vb2b8X3MdJu87/vGbi5ueHBgwdo27atRJw//fQTDh48+M49vMZILvNkhw0bhsjISPz8889vzRl1cnLCsWPH4OjoCGtra+Tm5mLHjh3gcDhvJY934XK5GDZsGFJSUjBu3Li3dvMa6osvvkBycjKmTZsmPvCxbds2CASCD9bNeDwe7OzsxBuex+OhY8eODX59Nzc3WFpaIj4+HpMnT37vazZt2hRr165FSEgIxo4di4kTJ6Jt27Z4+vQp9u3bh2vXrmHt2rUfLZ38nZeXF/79739j06ZNuHv3Lnx8fGBubo6nT59i//79ePr0Kb777rt3PldfXx+Ojo7YunUrdHV1xWexHT58WFwXdXJyQkxMDHbu3AlnZ2f88ccf2LFjBwQCgVTbWxo3btxAaGgoRo4cibt37yI6Ohrjxo374NSzhujRowe8vLywZMkSFBcXo2XLlkhNTQWPx/vgd8TDwwO7d+9GeHg4RowYgbq6OsTHx8PU1FT8D6B///7YsWMHtm/fDmdnZ5w/f15idg4AzJo1C+PHjxeXGQQCATZv3gxHR0f06dMHAoEAe/fuxcyZMzFnzhyYm5sjOTkZNTU1CAgIwL/+9S+MGDECX3/9NZ48eYLOnTvjwYMHiIqKQuvWrWFrawuhUAh9fX0sWLAAs2fPhoWFBX799VfcuXMHX3zxRYM+L2m3ubGxMW7fvo3ffvsNTk5OmDx5Mo4ePYrJkydj6tSpMDMzQ1paGg4cOCBRnmvs5JJkTU1N0bdvX2RkZGDo0KESy9auXYtVq1Zh1apVAP482v/NN9/gp59+kvpARf/+/ZGSkoLRo0fLHKuxsTH27NmDiIgILFiwAIaGhvj8889hYGAAAwOD9z6Px+NJHGi4e/cuJk+e3ODX19LSgo+PD5KSkj46Cu3VqxcOHjyIhIQExMXF4cWLFzAxMYGjoyP27dv3wYN1H7Jhwwa4u7vj6NGjWLp0Kaqrq2Fubg4vLy9ERkZ+MFmtXLkS3333HRISEvDixQs0a9YMfn5+mDNnDgAgKCgIr169wp49e7Bt2za0aNECI0eOFB/ZLisr++DsBWlMmjQJz549Q0hICMzMzDBjxgwEBQXJ1OffRUVFYe3atdi4cSOEQiEGDhyIzz77DEeOHHnvc/r06YNvv/0WCQkJ4oNdrq6u2LNnj7jGGRQUhJKSEiQkJKCurg79+vVDREQEZs6cKe6nU6dO2Lt3LzZu3Ih58+bB0NAQffv2RWhoKPT09KCnp4ekpCSsX78eEREREAqF6Nq1K/bu3Yt//etfAIDIyEjs2LFD/I+zWbNmGDp0KObOnQttbW1oa2sjISEBGzduREREBMrLy2Fra4uVK1c2+O9M2m0+depUrFmzBtOmTcPu3bvRvXt37N+/Hxs3bsSKFStQW1sLW1tbREREyLW8pGycxvBrtStWrEBOTg6OHTsmc1/Xr19HaWmpeNoKAAiFQvTr1w/Dhg1Tq/+g6sjBwUGqg4yyePLkCa5du4aBAwdK7Dl99dVXePTo0VsntxDyISp9Wu2ePXvwv//9DykpKTIf8HqtsLAQ8+bNQ3BwMNzc3MDn87F//35UVFRg3LhxcnkN0rhpaWkhPDwcAwcOhJ+fH7S1tZGZmYnTp0/L7XtINIdKJ9krV67gwoULCAgI+OjcWGkNGTIEpaWl+OGHH7Br1y7o6uqia9euSEpKQvv27eXyGqRxa9GiBeLi4rBt2zbMnTsXQqEQ7du3x7fffot///vfyg6PNDKNolxACCGNVaO+niwhhKg6SrKEEMIiSrKEEMIiSrKEEMIilZ5dQAjRXHXF/5Pp+boWDbvWMlsoyRJCVJOInUt9KprKJFkdvYadg9+YCQVPxPcH2bz/YuHqJv3RKfH9FW0mKDESxVrxx1+XxuTvW67ESBSL+5n0V/JSZyqTZAkhRAIjn6u2KRslWUKIapLTpTGVjZIsIUQlMTSSJYQQFtFIlhBCWKQmI1k6GYEQQlhEI1lCiGqiebKEEMIiNSkXUJIlhKgmOvBFCCHsUZcpXHTgixBCWEQjWUKIaqJyASGEsEhNygWUZAkhqommcBFCCItoJEsIISxSk5oszS4ghBAW0UiWEKKaqFxACCEsUpNyASVZQohKYhiaXUAIIeyhcgEhhLBITcoFNLuAEEJYRCNZQohq0rRyQW1tLdLS0sDj8VBdXQ1DQ0PY2dnB29sbxsbGbMZICNFEanJarVTlgmvXrqFfv36Ii4tDUVERamtrUVhYiF27dsHb2xt5eXlsx0kI0TSMSLabipBqJLt69WoEBQVh8uTJby1LTEzE6tWrkZKSIu/YCCGaTIEHvng8HtatW4dbt25BV1cXXl5eCA8Ph7m5OZYvX44ff/wRurq64vXDw8Mxfvx4qfqWaiR7//59BAQEvHPZxIkTkZ+fL9WLEUKI1BQ0kq2pqUFgYCBcXFxw8eJFHD9+HKWlpVi8eDEAIC8vD6tWrUJubq74Jm2CBaRMspaWlu8tCVy9ehVWVlZSvyAhhKiSwsJCfPLJJwgODoaenh7MzMwwfvx4XL58GQKBAPfu3UPnzp3/cf9SlQtmzpyJadOmYdiwYXBwcACXywWfz0dBQQGOHTuG5cuX/+MACCHknRRULmjXrh3i4+Ml2k6dOgVHR0fweDwIhUJER0cjJycHTZs2xZgxYxAYGAgtLelmwEqVZEeNGoWWLVsiNTUVqampqKqqApfLhZ2dHbZs2QIPD4+GvzMlsbRshu2x69G3jweEwnok/3AICxauRH29ehzJfJ9+w/tiUfRCCGoF4raLJ3/BurkblBgV+yzat8Cny79Aa+f2qK3k48oPZ3Fx209gGEbZocnV3aevsOn0NdwpfAVdbS14tLfGfB8XmBk2wf7f8pGcdRfFFTWwaKqPCe728He3V3bIH6eEkxEYhsF3332Hc+fOISkpCcXFxXBzc0NAQAA2bdqEO3fuIDg4GFpaWggMDJSqT6mncLm7u8Pd3f0fB68q9iVvx5PCIti06QZr6+Y4fGg35s75Ehs3bVd2aKyy72qPM4f+i2/nb1R2KAqjZ9AEE/eE4/6FPKQEfQcD86b4fNd8aGlrI2PzIWWHJzc1dUKEJGVglGt7bP28D6oEQiw9nIXlR7MxxrU9Ys7mYfsX/dCppTluPnmJabvPon1zE/Roq9plPkVfu6CyshKLFi3CrVu3kJSUBAcHBzg4OMDLy0u8jpOTEyZNmoS0tDSpk6xU492DBw9KPE5ISICfnx8mTpyI48ePN+BtKFf79rbo188T4YsiwOfX4MGDh4hYsxmzZk5Rdmisc+hqj3s37ik7DIX6Vw8HGFoYI+3r3ajj16LsSTEytx5Fj4kDlR2aXD0tq4a9tRmC+jpCV0cbpgZN4OfaAVf/eIG+Dq1wYt5wdGppDmG9CKXVteAAaKqvp+ywP04kku3WAA8fPsSYMWNQWVmJ1NRUODg4AADS09Oxf/9+iXUFAgH09fWl7luqJBsZGSm+HxcXh8TERAwZMgSenp6IiIh4KwhV1amTPV6+fIWiomfitjt37qFNm9YwMVHfEyo4HA7sOneA+wA3JF/ag32/JWHe2jkwMjFSdmis4mhroV4gRH3dXyMiRiSCUXNT6BsbKDEy+bK1MMa2iX2h/UaNMP32I3RsYQYAMGyii9+Ly+G++iBCkjMxtkcHfPL/y1SagmYXlJWVYdKkSejWrRt27doFc3Pzv0JgGERGRuLSpUtgGAa5ubnYs2dPg2YXSFUueLN+9eOPP2Lr1q1wcnICAHh4eGDhwoXw9/eX+kWVpWlTI1RVVUu0VVfzAQBGRoYoKytXRlisM2lmgoJb93Eh7QJWBq2GibkxFkSFYdHmBVgyeZmyw2PNoyv3IKwRYNDC8cjYfBgGzYzhFfRvAICuvh5qyqs/0kPjwzAMtp3NQ8a9J0iY8teIvZWZEbKWjsW9p6WYu/8CzA31MaVXRyVGqjoOHTqEwsJCnDhxAidPnpRYlpubi0WLFmHFihV49uwZLCwsMHv2bIwcOVLq/qVKshwOR3y/oqICjo6O4scuLi548eKF1C+oTFVV1TAw4Eq0vX5cUVGpjJAUorS4FP/xCxU/fl74AnFr4rHlp83gGnLBr+IrMTr21JRXI2nyevh8PRHzsqJR8vszXD90Aa2c26tlgq2sqcPyo9m4XViChCkDYWdlKl6mq/3nKNexlTk+d7fHibw/VD/JKujA15QpUzBlyvtLhv7+/jINIqUqF9TV1eHo0aO4f/8+XF1dkZubK16WlZXVaObJ3rp1FxYW5mje3ELc1rGjPR49KkR5eYUSI2NX20/aIjB8qkSbrp4uGBEDYZ1QSVGxT1tXG1ra2vjePwLrnWcg3nc5RPUiPL/3GHU1go930Ig8KqnAhLjTqKytww/TfcQJdu+lu1hw8BeJdevq62HMbQQ1WTU5rVaqJDt69Gjs2bMHvr6+OHPmDLZu3QoASElJwcyZMzF16tSP9KAaCgoe4OLFbGza+A2MjAxha2uDJYvnYHfiPmWHxqqK0gqMnDwC42aMhZa2Fpq3tMT0JYE4dfA06gR1yg6PPRwOApLC4TK+LwCgRWdb9AnxRdaukx95YuNSzhdg+vfn0NXGArET+8HMsIl4mWsbS5zjPcGpmw8hEjHIffgCP2Tdw9juHZQYsZQUeOCLTRymARMGBQIBeDweKisr4enpifT0dADAoEGDZA5ER6+VzH1Io3lzC0RvjkC/vp4QiURISkpF+OIIiBS4UYSCJ+L7g2x8FPKaTj27YNrCqbB1aANBbR3O/3QeO9fEo65WcUk2/dEp8f0VbSYo5DXbuH0Cn2UT0aytNapeliMr4SR+SzytkNd+bcUfyeL7/H3yP3Fn7688bDx9Dfq62uCAI7Hs0hI/ZNx9gm1n8/DkVSVamBpiSq+OGOZkK/c4/o772TcyPZ9/aqtsr+8TItPz5aVBSZZNikqyqkAZSVYVKCPJqgK2k6yqkjnJnoiW7fWHfCXT8+WFfhmBEEJYRL+MQAhRTSpUV5UFJVlCiGpSoRkCsmhwueDNC6lkZGTgxo0bcg2IEEIAqM3sggYl2bNnz6J3794AgJiYGMyePRsBAQE4cOAAK8ERQjSYJs2TfS02NhZz5879/6lPSdiyZQuSk5MRFxfHVnyEEE2lJiPZBtVkHz58iHHjxuH27dvg8/nw8vKCjo4OiouL2YqPEEIatQYlWS6Xi5cvX+Ls2bNwdXWFjo4OeDwezMwawRV9CCGNiwrt8suiQUl2zJgx8PX1RXl5OaKjo3Hz5k0EBgY2mtNqCSGNiArt8suiQUl29uzZcHNzQ5MmTeDs7IyioiKsXLkSgwcPZis+Qoim0sQkC0DiJ2hatGiBFi1ayDUgQggBAKjGGf8yo5MRCCGqSU1GsnTtAkIIYRGNZAkhqklNRrKUZAkhqkkTp3ARQojC0EiWEEJYRLMLCCGERWoykqXZBYQQwiIayRJCVJOajGQpyRJCVBPNLiCEEPYwIjrwRQgh7KFyASGEsEhNygU0u4AQQlhEI1lCiGqimiwhhLCIarKEEMIiSrKEEMIiunYBIYSwSE1GsjS7gBBCWEQjWUKIaqLZBYQQwiI1ORmBwzBqUl0mhKiV6nVTZHq+wcLdcopENjSSJYSoJEZNDnxRkiWEqCaqycqXjl4rZYegMELBE/F9E6P2SoxEscoq74vvy7or2Ji8udtac2GvEiNRLP3eAcoOQSWoTJIlhBAJanLgi5IsIUQ1UbmAEEJYRAe+CCGERWoykqXTagkhqokRyXZrAB6PhylTpsDNzQ1eXl5YsGABSkpKAADXr1/H2LFj4eLiggEDBuDgwYMN6puSLCFEo9XU1CAwMBAuLi64ePEijh8/jtLSUixevBhlZWWYPn06fH19cfnyZURERCAyMhI3btyQun9KsoQQ1SRiZLtJqbCwEJ988gmCg4Ohp6cHMzMzjB8/HpcvX8bp06dhamqKCRMmQEdHBx4eHhg+fDiSk5Ol7p+SLCFEJTEikUw3abVr1w7x8fHQ1tYWt506dQqOjo7Iz8+Hvb29xPodOnQAj8eTun9KsoQQ1aSgkeybGIZBVFQUzp07hyVLlqCqqgpcLldiHX19fVRXV0vdJ80uIISoJgXPLqisrMSiRYtw69YtJCUlwcHBAVwuFxUVFRLr1dTUwNDQUOp+aSRLCNF4Dx8+xJgxY1BZWYnU1FQ4ODgAAOzt7ZGfny+xbkFBAezs7KTum5IsIUQ1KWgKV1lZGSZNmoRu3bph165dMDc3Fy/z9vZGcXExEhMTUVdXh6ysLBw7dgxjxoyRun8qFxBCVJOCygWHDh1CYWEhTpw4gZMnT0osy83NRUJCAiIiIhAdHQ1zc3MsXboUPXv2lLp/SrKEEJXEKCjJTpkyBVOmvP+qcF26dMH+/fv/cf+UZAkhqklNTqulJEsIUU1qcoEYOvBFCCEsopEsIUQ1UbmAEEJYREmWEELYwzAamGQfP36Mu3fvorq6GoaGhrCzs4ONjQ1bsRFCNJkmjWRfvHiBpUuXIjMzE8bGxuByueDz+SgrK4O7uzuioqIkzpIghBCZqUmSlWp2wYoVK2BoaIiLFy8iOzsb58+fR3Z2Ni5cuABzc3MsX76c7TgJIaRRkmokm5WVhczMzLeuPGNpaYlVq1ahf//+rARHCNFcijrji21SJVl9fX1UVla+8/JepaWlMDAwkHtghBANp0lJdsSIEQgKCsL06dNhZ2cHLpeLmpoa5OfnIzY2FiNHjmQ7TkKIplGPE76kS7JhYWGIiYnB+vXr8fTpU3G7lZUVRo8ejeDgYNYCJIRoJo0qF2hpaSEkJAQhISGorKwU/ySDsbEx2/ERQjSVJiVZAKitrUVaWhp4PJ54nqy9vT0GDRpEyZYQQt5Dqilc165dQ79+/RAXF4eioiLU1taisLAQ8fHx8Pb2Rl5eHttxEkI0jUjGm4qQaiS7evVqBAUFYfLkyW8tS0xMxOrVq5GSkiLv2FhhadkM22PXo28fDwiF9Uj+4RAWLFyJ+vp6ZYemEM0szJH+31TMDlmEixeylR2O3HEsbaA3YDy0rNoA9fWo//0mBGf3A/xK8TpaLdujyWcLwd84XYmRytfdR8+w6UA6bv9RBF0dbXg4tkPoOG+YNTXA6r1pOPLLdeho/zWmCh3nDb++3ZQY8cepS01WqpHs/fv3ERAQ8M5lEydOfOuHxlTZvuTtqKysgk2bbvDwGoaBA3th7pwvlR2WQrj3dEX6f1PRrn0bZYfCDh1dNBk7D6InBeBvnQv+riXgcI3QZOg08SraXXqjybj54OjoKjFQ+aoR1CH4u33o2qE1zm6ah0Mrg1BWycey3ccAALd+L8KygKHI2rZQfFP1BAtAbUayUiVZS0vL95YErl69CisrK7kGxZb27W3Rr58nwhdFgM+vwYMHDxGxZjNmzXz/T0+oi88+H434hCisWrlR2aGwhmPcDMyLR6j75SggqgdqqiC8dh5aNvYAAL2hU6HTtQ/qfjmi3EDl7GlJOextrBA0vDd0dbRhamQAv77dcDX/IQR1QuQ/eY5Oti2VHWaDMSJGppuqkKpcMHPmTEybNg3Dhg0T/xY5n89HQUEBjh071mhOq+3UyR4vX75CUdEzcdudO/fQpk1rmJgYo6ysXInRseu/6Zk4kHIU9fX12P19tLLDYQVT8hS1B6Mk2rQdukP09A8AQN2Fw2AqXkHLxkEZ4bHG1roZYuZ+JtF2JucOOraxxt1HzyCsr0fM0Qzk5j+CkUETjOrljMk+HtDS4igpYimp0GhUFlIl2VGjRqFly5ZITU1FamqqeAqXnZ0dtmzZAg8PD7bjlIumTY1QVVUt0VZdzQcAGBkZqnWSff68WNkhKJxu79HQ7uCMmuS1AACm4pWSI2IfwzDYduQ8Mq7nY/eCL1BcVonuDm3w+cAeWDd9FHgPn2JeTCq0OBxM/rRx/N02dlJP4XJ3d4e7uzubsbCuqqoaBgZcibbXjysqKt/1FNIY6elDb+g0aFnboiZ5LZjix8qOSCEq+bVYtvsY7vxRhN0LvoBd6+awa90cHo7txOt0adcKEwe54dTl2yqfZBk1GclK/RtfiYmJCA4ORmxsLKqrJUeD06c3jqO0t27dhYWFOZo3txC3dexoj0ePClFeXqHEyIi8cEwtof/FMnCacFHz/Tcak2AfPS/B56t3oYpfix+WToNd6+YAgLO5d3EwI0diXYFQiCZ6jeB6/Zp04Gvr1q3Yu3cvbGxscPToUUycOBEVFX8lpStXrrAWoDwVFDzAxYvZ2LTxGxgZGcLW1gZLFs/B7sR9yg6NyEMTAzTxXwBRYQFqUzZKTNtSZ+VVfHz5bRK6tm+N2Hmfw6zpXxdsYhgG36acQfadB2AYBtfvP8YP6Zfh10f1ZxcwItluqkKqf2eHDx/Gzp070b59e8ydOxfBwcGYM2cO4uPjoaXVuH7wdpz/dERvjkDBvSyIRCIkJaVidcR3yg6LyIGOUy9omViA4+AGrkMPiWX8qJlKiop9R365jqKScpy+cgdncu5ILMvathCh470RkXQCz15VwMLEEDNH9sG/PbooKdoGUKFEKQsOI8UP6bi6uiIn569djqqqKvj7+6NPnz4ICwuDi4sLcnNzZQpER6+VTM9vTISCJ+L7JkbtlRiJYpVV3hffr16n/tPmXjNYuFt8v+bCXiVGolj6vd89t15aL7z7yvR8yzMZMj1fXqQahtrY2CAj46+ADQ0NsXnzZqSkpODQoUPgcFR8KgghhCiJVEl25syZmDNnDtatWydua9euHTZu3Ijly5ejpqaGtQAJIZpJo2qyPj4+aN26NZ48eSLR3rdvX+zduxc7duxgJThCiOZSpUQpC6nncTg6OsLR0fGtdmdnZ8TGxso1KEIIAaMeZchGMFmOEKKJNG4kSwghisSI1GMk27gmuRJCSCPT4CT75sWtMzIycOPGDbkGRAghgPrMLmhQkj179ix69+4NAIiJicHs2bMREBCAAwcOsBIcIURzMQxHppuqaFCSjY2Nxdy5c///dNQkbNmyBcnJyYiLi2MrPkKIhlKXkWyDDnw9fPgQ48aNw+3bt8Hn8+Hl5QUdHR0UF2vetUoJIexSlwNfDUqyXC4XL1++xNmzZ+Hq6godHR3weDyYmZmxFR8hREN9/KoqjUODkuyYMWPg6+uL8vJyREdH4+bNmwgMDMTUqVPZio8QQhq1BiXZ2bNnw83NDU2aNIGzszOKioqwcuVKDB48mK34CCEaSiPLBQAkfoKmRYsWaNGihVwDIoQQQIOTLCGEKIJG1mQJIURRaCRLCCEsUqUTCmRB1y4ghBAWUZIlhKgkZZzxVVJSAm9vb2RnZ4vbli9fjs6dO8PFxUV8S0lJkbpPKhcQQlSSSMHlgpycHISHh+Phw4cS7Xl5eVi1ahVGjRr1j/qlkSwhRCUp8gIxhw8fRmhoKObNmyfRLhAIcO/ePXTu3Pkfvw9KsoQQlcSIODLdGqJXr144c+YMhg4dKtHO4/EgFAoRHR0NT09P+Pj4YOfOnRCJpK9HULmAEKKSFDlP1tLS8p3tFRUVcHNzQ0BAADZt2oQ7d+4gODgYWlpaCAwMlKpvGskSQsh7eHl5Yc+ePXBzc4Ouri6cnJwwadIkpKWlSd0HJVlCiEpSZLngfdLT07F//36JNoFAAH19fan7oCRLCFFJIoYj000eGIZBZGQkLl26BIZhkJubiz179mD8+PFS90E1WUKISlKFM768vb2xaNEirFixAs+ePYOFhQVmz56NkSNHSt0HJVlCiEpS1gVi7t69K/HY398f/v7+/7g/SrKEEJWk6JMR2EI1WUIIYRGNZAkhKkkVarLyQEmWEKKS1OWi3RyGUZe3QghRJ1da+8r0/O6Pj8glDlnRSJYQopKoXEAIISxSl9kFKpNkdfRaKTsEhREKnojvz7P95/PvGpuo3/86PfG/VtKfMdPYDXz21wWe64r/p8RIFEvXop2yQ1AJKpNkCSHkTepysIiSLCFEJVG5gBBCWEQHvgghhEX/8LcQVQ4lWUKISmKgHiNZunYBIYSwiEayhBCVJFKT6QWUZAkhKkmkJuUCSrKEEJWkLjVZSrKEEJWkLrML6MAXIYSwiEayhBCVROUCQghhkbqUCyjJEkJUEiVZQghhEZULCCGERSL1yLE0u4AQQthEI1lCiEqiM74IIYRFanLpAkqyhBDVpJGzCx4/foy7d++iuroahoaGsLOzg42NDVuxEUI0mIijQeWCFy9eYOnSpcjMzISxsTG4XC74fD7Kysrg7u6OqKgomJubsx0rIUSDqEu5QKrZBStWrIChoSEuXryI7OxsnD9/HtnZ2bhw4QLMzc2xfPlytuMkhJBGSaqRbFZWFjIzM2FoaCjRbmlpiVWrVqF///6sBEcI0VwaVZPV19dHZWXlW0kWAEpLS2FgYCD3wAghmk1dTkaQKsmOGDECQUFBmD59Ouzs7MDlclFTU4P8/HzExsZi5MiRbMdJCNEwGjVPNiwsDDExMVi/fj2ePn0qbreyssLo0aMRHBzMWoCEEM2kLge+pEqyWlpaCAkJQUhICCorK1FVVQUulwtjY2O24yOEaCiNKhcAQG1tLdLS0sDj8cTzZO3t7TFo0CBKtoQQ8h5STeG6du0a+vXrh7i4OBQVFaG2thaFhYWIj4+Ht7c38vLy2I6TEKJhRDLeVIVUI9nVq1cjKCgIkydPfmtZYmIiVq9ejZSUFHnHxgpLy2bYHrsefft4QCisR/IPh7Bg4UrU19crOzRWtXa0he+ySWjZ8V+oqxHg2s9Z+CkyGfUCobJDY41us6bo/vNq3PnPDpT+ehsA0HrKYNhMHwo9KzMInr3Co7gTeJxwSsmRyo6X/z98uzUet+/mQ1dXF55u3bBg9pcwMzVBWvp5xCb8gOcvimHRzBxfjB+F8aOGKTvkj1KXmqxUI9n79+8jICDgncsmTpyI/Px8uQbFpn3J21FZWQWbNt3g4TUMAwf2wtw5Xyo7LFZxOBwEJizA9RPZWNI1EJtGLIFDHycMCBqh7NBYY9LDAd1/Xg2DttbiNovB3dBu4XjcDNqMjHaTcHPmFnRYNhFmXo5KjFR2NbW1mDn/azh36YiMYz/gaNJ2lJaVY+maTcj/3+9YHvkdVi+eh+wzh7B6yX+wdvN25Fy7qeywP0rEke2mKqRKspaWlu8tCVy9ehVWVlZyDYot7dvbol8/T4QvigCfX4MHDx4iYs1mzJo5RdmhsYprYggTK3NwtDh4PSuGETGo49cqNzCWWI/rA8fY2bgfuV+ivfj0VfziGoyKGw/A0daCnnlTgGFQV1alpEjlo+jpCzjYtcPMKZ9DV1cXpibGGDdyKHKu3cTvD59AWF8PEcOAYRhwOBxoa2lDr4mussP+KI0qF8ycORPTpk3DsGHD4ODgIL52QUFBAY4dO9ZoTqvt1MkeL1++QlHRM3HbnTv30KZNa5iYGKOsrFyJ0bGnurQS5+N/xsglARixeCK0dbSRd/oyMnalKTs0VpScu45nP14EUy8Cds6VWFZfVQOD9i3gnrkRWjra+CP2OCpv/q6UOOWlbZvW2L5xlUTb6fMX0cnBDl7urujq+AkCZsyHtrYW6utFCA0JRJeODkqKVnqqlChlIVWSHTVqFFq2bInU1FSkpqaKp3DZ2dlhy5Yt8PDwYDtOuWja1AhVVdUSbdXVfACAkZGh2iZZDoeDuhoBfly+G9kHzsGyjTWm7JiPT+eNxYlNB5QdntwJXpR9cDn/j+c43yYARo5t4PR9GOqKy/DH1p8UFB27GIbBlrg9yPglG4nb1qOurg6tWlgjaPLn6O7SBb/+dhWhyyJh184WXu6uyg5XI0g9hcvd3R3u7u5sxsK6qqpqGBhwJdpeP66oqFRGSArRxacHnIa4Ye3A+QCAp/mPcWpzKkavmKyWSfZjGOGfBzkrrv8Pj+JOwHpML7VIspVVVVgaEYXbd/ORuG097Nu3xZpNMdDT04NHDxcAQF9PNwwd1A8Hj55Q+STLqFBdVRZS1WQPHjwo8TghIQF+fn6YOHEijh8/zkpgbLh16y4sLMzRvLmFuK1jR3s8elSI8vIKJUbGLrNWFtDRk6zBiYT1qK9T35kF72ITNBSdd86RaNPS04GwtPH/g334uBD+0+agsqoaKbuiYd++LQCg6NkL1NXVSayro6MNXV3Vv16/utRkpUqykZGR4vtxcXFITEzEkCFD4OnpiYiICOzfv/8Dz1YdBQUPcPFiNjZt/AZGRoawtbXBksVzsDtxn7JDYxUv4zqMm5ti0CxfcLQ4aGbTHN4ho3Dl8EVlh6ZQpZfuwPLTHmg+oifA4cCkhwNsvhyCx4mnlR2aTMrKKzDtq0Vw7tIJO6NWw8zURLysX6+eOPnfTPySnQOGYXA59waOnzqLYYNV/8p5ykiyJSUl8Pb2RnZ2trjt+vXrGDt2LFxcXDBgwIC3Bp0fI9W/M4b5a8bajz/+iK1bt8LJyQkA4OHhgYULF8Lf379BL6ws4/ynI3pzBAruZUEkEiEpKRWrI75TdliselbwBPFT12NI6HgMmDEc/PJq5By5iFObU5UdmkJV3HiAvMBNaBfuj46bglDzuBj3vv4ez3/KUnZoMjmSdgZFz57j1NlMnDp3QWLZ5fTDqKmpQWRULF68LEELq+b4OjQE/bxUv/Sn6HmyOTk5CA8Px8OHD8VtZWVlmD59Or766iuMHz8ely9fRnBwMBwcHMQ58GOkSrKcN34GoqKiAo6Of80rdHFxwYsXL6R9H0r3/Hkx/D8LUnYYCnfvl5u494vqz42Ut/9ajZd4XHz6KopPX1VSNOyY5D8ak/xHv3f5hLEjMWFs47tSniLnuh4+fBjR0dEICwvDvHnzxO2nT5+GqakpJkyYAODPQeXw4cORnJwsdZKVqlxQV1eHo0eP4v79+3B1dUVubq54WVZWVqOZJ0sIIe/Sq1cvnDlzBkOHDpVoz8/Ph729vURbhw4dwOPxpO5bqpHs6NGjsWfPHty7dw9CoRDl5eVITExESkoK1q5di0WLFkn9goQQIg1FHryytLR8Z/vr6apv0tfXR3V19TvXfxepkuw333wDABAIBODxeKis/PNobLNmzbBhwwYMGjRI6hckhBBpqMIMAS6Xi4oKyZlHNTU17/yVmPdp0DwOPT09iToEJVdCCFtU4QIx9vb2+OWXXyTaCgoKYGdnJ3UfUtVkCSFE0VThAjHe3t4oLi5GYmIi6urqkJWVhWPHjmHMmDFS96H6M5IJIRpJFcoFZmZmSEhIQEREBKKjo2Fubo6lS5eiZ8+eUvdBSZYQQt5w9+5dicddunSR6YSrBpcL3ry4dUZGBm7cuPGPX5wQQt6HkfGmKhqUZM+ePYvevXsDAGJiYjB79mwEBATgwAHNu8gIIYRdIjAy3VRFg5JsbGws5s6d+/+noyZhy5YtSE5ORlxcHFvxEUI0lLpcIKZBNdmHDx9i3LhxuH37Nvh8Pry8vKCjo4Pi4mK24iOEaCjVGYvKpkFJlsvl4uXLlzh79ixcXV2ho6MDHo8HMzMztuIjhGgoVRqNyqJBSXbMmDHw9fVFeXk5oqOjcfPmTQQGBmLq1KlsxUcIIY1ag5Ls7Nmz4ebmhiZNmsDZ2RlFRUVYuXIlBg8ezFZ8hBANpUq/OCuLBs+TffMnaFq0aIEWLVrINSBCCAGgUjMEZEEnIxBCVJJ6pFhKsoQQFaWRB74IIURR1KVcQFfhIoQQFtFIlhCiktRjHEtJlhCioqgmSwghLFKXmiwlWUKISlKPFEtJlhCiotSlXECzCwghhEU0kiWEqCRGTQoGlGQJISpJXcoFlGQJISqJZhcQQgiL1CPFUpIlhKgodRnJ0uwCQghhEY1kCSEqiQ58EUIIi9RlCheHYRj1eCeEELUy1dZPpucn/J4qp0hkQyNZQohKUpeRLCVZQohKopqsnOnotVJ2CAojFDwR37/S2ld5gShY98dHxPert89RXiAKZjBjs/j+PFt/JUaiWFG/71d2CCpBZZIsIYS8SaQmh4soyRJCVJJ6pFhKsoQQFaUuZ3xRkiWEqCSaXUAIISxSl9kFdO0CQghhEY1kCSEqiWqyhBDCIqrJEkIIi9SlJktJlhCiktTl2lV04IsQQlhEI1lCiEqiA1+EEMIiqskSQgiLaHYBIYSwiMoFhBDCIkXOLkhLS0NoaCiaNGkibhs0aBA2bNggc98NTrKVlZWoqqqCoaEhjIyMZA6AEEKULS8vDyNHjkRkZKTc+5YqyYpEIiQmJiIpKQlFRUXidmtra/j5+WHWrFngcDhyD44QorkUeeArLy8PQ4YMYaVvqZLs2rVrcenSJYSGhqJDhw7gcrng8/koKChAbGwsqqurERYWxkqAhBDNpKgDXyKRCLdu3QKXy0V8fDzq6+vRt29fhIaGwsTEROb+pUqyx44dw8GDB9G6dWuJdnt7e3Tp0gX+/v6UZAkhcqWoA18lJSXo1KkTfHx8EB0djVevXmHhwoUICwvDzp07Ze5fqiQrFArRvHnzdy4zNzdHfX29zIEQQsibFHXgy8LCAsnJyeLHXC4XYWFhGDduHCorK2U+9iTVabVubm5YunQpiouLJdpLSkqwbNkyuLu7yxQEIYT8nQiMTDdp8Xg8fPvttxJJXSAQQEtLC3p6ejK/D6lGsqtWrcKcOXPQu3dvmJiYwMDAAHw+H6WlpXB1dUV0dLTMgRBCiDKYmpoiOTkZJiYmmDJlCp4/f44NGzZg1KhRikuy5ubm2Lt3Lx4+fIj8/HxUVVXBwMAAdnZ2aNOmjcxBEELI3ynqwJe1tTV27NiBTZs2ITY2Fk2aNMGwYcPkdpypQfNktbT+rC5wOBxoaWmJHxNCiLyJFHgygpubG/bv389K31Il2RcvXmDp0qXIzMyEsbGxeApXWVkZ3N3dERUVBXNzc1YCJIRoJvU4qVbKA18rVqyAoaEhLl68iOzsbJw/fx7Z2dm4cOECzM3NsXz5crbjJIRoGEUd+GKbVCPZrKwsZGZmwtDQUKLd0tISq1atQv/+/VkJjhCiuVQpUcpCqpGsvr4+Kisr37mstLQUBgYGcg2KEELUhVQj2REjRiAoKAjTp0+HnZ0duFwuampqkJ+fj9jYWIwcOZLtOAkhGkZdfuNLqiQbFhaGmJgYrF+/Hk+fPhW3W1lZYfTo0QgODmYtQHmztGyG7bHr0bePB4TCeiT/cAgLFq5U67PWdMyN8clP6/BH2DZUXLopsUy3uRk6nYrC4zV78PLgWSVFKD93X5QjKvMu7jwrh642Bz3bWGB+309gxtVDev5TxGXfx5Oyahjr62Jkp9b4smd7aKnhxY1aO9rCd9kktOz4L9TVCHDt5yz8FJmMeoFQ2aFJTV3KBVIlWS0tLYSEhCAkJER8qUMulwtjY2O245O7fcnb8aSwCDZtusHaujkOH9qNuXO+xMZN25UdGiuMun8C2+/mQN+2xdsLORy03TIPOuZNFR8YC2qE9Qg5nIPRnVtji68rqgRCfH0qDytO5SHIowO+PnkD64Y5o1dbS/xeUoXZR3LA1dPGF65tlR26XHE4HAQmLMB/Y3/CNv+VMLYyw8zkJRhQUoEzWw4pOzypadwvI9TW1iItLQ08Hg/V1dUwNDSEvb09Bg0a1GiSbfv2tujXzxM2bbqBz6/BgwcPEbFmM9auWaKWSbaZX3+0DP0MjyO+R/vYtydWt5w3HoKilxAUvlRCdPL3tJwPe8ummN6zA7S1ODDl6mFMFxt8ffIGCsv5GONkgz7t/rwGR7tmRujfoTmuPn6ldkmWa2IIEytzcLQ4wP8P0hkRgzp+rXIDayB1KRdIdeDr2rVr6NevH+Li4lBUVITa2loUFhYiPj4e3t7eyMvLYztOuejUyR4vX75CUdEzcdudO/fQpk1rmJg0jn8UDVGWkYs8rxl4deyXt5Y19ewMsxG98HDxDiVExg5bcyNsG9Ud2lp/7f6n5z9FRytjDLKzRmjfjuL2GmE9Lj54gY5W6rfdq0srcT7+Z4xcEoAN95KwIisGLx4UIWNXmrJDaxCNmsK1evVqBAUFYfLkyW8tS0xMxOrVq5GSkiLv2OSuaVMjVFVVS7RVV/MBAEZGhigrK1dGWKwRvih9Z7tOMxPYbvwK96evg6i6RrFBKQjDMIj5NR+Z/3uOXeMkL2BUJRAi7HgumuhoY2I3W+UEyCIOh4O6GgF+XL4b2QfOwbKNNabsmI9P543FiU0HlB2expFqJHv//n0EBAS8c9nEiRORn58v16DYUlVVDQMDrkTb68cVFe+eoqaO2m6ei+cJx1Gdd1/ZobCislaI0OPX8DOvELvGucPO4q+a8+8llZi0Pwv1IgZxfm4w1FO/n7nr4tMDTkPc8GvSGdQLhHia/xinNqfCK8Bb2aE1CMMwMt1UhVRJ1tLS8r0lgatXr8LKykquQbHl1q27sLAwR/PmFuK2jh3t8ehRIcrLK5QYmeLotbRA056OaDF3PJxvJcP5VjL0WlngX2uC0CFxibLDk9mj0mpM3PcrqgRCJH/uKZFgLzx4gYB9WfC0tcC20d1hrK+rxEjZY9bKAjp6ku9NJKxHfV3jmVkAaFi5YObMmZg2bRqGDRsGBwcHiZ+fOXbsWKM5rbag4AEuXszGpo3fYMbMBbCwMMeSxXOwO3GfskNTGEFhMa52GCfR1uXSThRu2t/op3CV19RheupvcLNphuWDO0tMzbpRVIr5x65i8QBH+HZu/YFeGj9exnUMW+CPQbN88d/tR2HeyhLeIaNw5fBFZYfWIBo1u2DUqFFo2bIlUlNTkZqaKp7CZWdnhy1btsDDw4PtOOVmnP90RG+OQMG9LIhEIiQlpWJ1xHfKDovIwdFbj/G0ogan7z3FmfynEst62JhDWM9g/fk7WH/+jrjdpZUZto3qruhQWfWs4Anip67HkNDxGDBjOPjl1cg5chGnNqcqO7QGUeRVuNjEYVSkeKGj10rZISiMUPBEfP9Ka1/lBaJg3R8fEd+v3j5HeYEomMGMzeL782z9lRiJYkX9LtulAx2tZPvFlVvPsmV6vrzQBWEJIYRF6ndolRCiFtSlXCBVknVyckJdXd0H17lz584HlxNCSENo1IGv77//HtOmTcNXX32FTp06sR0TIYRo1kjWxcUFixcvRmpq6jvP+iKEEHlTl5Gs1Ae+/Pz80LZtWzx79uzjKxNCiIxEDCPTTVU06MBXZGQkW3EQQohaotkFhBCVpC7lAkqyhBCVxDAiZYcgF5RkCSEqSZUu8iILSrKEEJWkImf8y6zBp9W++YODGRkZuHHjhlwDIoQQQH0uddigJHv27Fn07t0bABATE4PZs2cjICAABw7Q1dYJIeRdGpRkY2NjMXfu3P+/RGAStmzZguTkZMTFxbEVHyFEQ6nLLyM0qCb78OFDjBs3Drdv3wafz4eXlxd0dHRQXFzMVnyEEA2lSicUyKJBSZbL5eLly5c4e/YsXF1doaOjAx6PBzMzM7biI4RoKI2cJztmzBj4+vqivLwc0dHRuHnzJgIDAzF16lS24iOEaChV2uWXRYOS7OzZs+Hm5oYmTZrA2dkZRUVFWLlyJQYPHsxWfIQQDaVKMwRk0eB5su7uf/0kRIsWLdCiRQu5BkQIIeqETkYghKgkjSwXEEKIomjk7AJCCFEUGskSQgiLNPbAFyGEKIK6jGQbfIEYQggh0qORLCFEJdGBL0IIYZFGnlZLCCGKQiNZQghhkboc+KIkSwhRSepSLqDZBYQQjffy5UvMmjUL3bt3h7u7OyIiIiAUCuXSNyVZQohKUuQvI8ydOxcGBga4cOECUlNTcenSJSQmJsrlfVCSJYSoJEUl2T/++AO//fYbwsLCwOVyYWNjg1mzZiE5OVku70NlarJCwRNlh6AU3R8fUXYISmEwY7OyQ1CKqN/3KzuERkNRFdn8/HyYmprCyspK3Na+fXsUFhaivLwcxsbGMvWvMkmWEELepKiBV1VVFbhcrkTb68fV1dUyJ1kqFxBCNJqBgQH4fL5E2+vHhoaGMvdPSZYQotHs7OxQWloq8avb9+/fh7W1NZo2bSpz/5RkCSEazdbWFq6urlizZg0qKyvx6NEjxMTEwM/PTy79cxh1Oa2CEEL+oeLiYqxcuRLZ2dnQ0tKCr68vQkNDoa2tLXPflGQJIYRFVC4ghBAWUZIlhBAWUZIlhBAWUZIlhBAWqX2S5fF4mDJlCtzc3ODl5YUFCxagpKQEALB8+XJ07twZLi4u4ltKSoqSI5aftLQ0dOrUSeL9hYWFAQCuX7+OsWPHwsXFBQMGDMDBgweVHK18/PTTTxLv18XFBZ07d0bnzp0BqOc2Lykpgbe3N7Kzs8VtH9u+hw8fhre3N5ydnTF69Gjk5uYqOmzNwagxPp/PeHl5MZs3b2Zqa2uZkpIS5ssvv2SCgoIYhmGYUaNGMYcOHVJylOxZu3YtEx4e/lZ7aWkp4+bmxiQlJTF1dXXMr7/+yri4uDDXr19XQpTsevr0KePl5cUcOXKEYRj12+ZXrlxhBg0axNjb2zNZWVkMw3x8+2ZlZTEuLi7MlStXGIFAwOzevZtxd3dnqqurlflW1JZaj2QLCwvxySefIDg4GHp6ejAzM8P48eNx+fJlCAQC3Lt3TzzCUUd5eXnvfH+nT5+GqakpJkyYAB0dHXh4eGD48OFyu+qQqmAYBmFhYejXrx9Gjhypdtv88OHDCA0Nxbx58yTaP7Z9Dx48iGHDhsHV1RW6urqYPHkyzMzMkJaWpoy3ofbUOsm2a9cO8fHxEhOKT506BUdHR/B4PAiFQkRHR8PT0xM+Pj7YuXMnRCKREiOWH5FIhFu3buH8+fPo378/+vTpg6+//hplZWXIz8+Hvb29xPodOnQAj8dTUrTsOHr0KAoKChAeHg4AarfNe/XqhTNnzmDo0KES7R/bvgUFBRqx/VWFWifZNzEMg6ioKJw7dw5LlixBRUUF3NzcEBAQgIyMDGzYsAF79+5FQkKCskOVi5KSEnTq1Ak+Pj5IS0vD/v378fvvvyMsLOydVx3S19dHdXW1kqKVP5FIhNjYWMyYMQNGRkYAoHbb3NLSEjo6b19I72PbVxO2vyrRiEsdVlZWYtGiRbh16xaSkpLg4OAABwcHeHl5iddxcnLCpEmTkJaWhsDAQCVGKx8WFhYSu/9cLhdhYWEYN24cRo8ejZqaGon1a2pq5HLFIVWRnZ2N58+fS5x/7uXlpdbb/DUul4uKigqJtje3L5fLfef2NzMzU1iMmkTtR7IPHz7EmDFjUFlZidTUVDg4OAAA0tPTsX+/5AWUBQIB9PX1lRGm3PF4PHz77bcSV4gXCATQ0tKCk5MT8vPzJdYvKCiAnZ2dosNkzalTp+Dt7Q0DAwNxm7pv89fs7e0/uH3t7OzUfvurErVOsmVlZZg0aRK6deuGXbt2wdzcXLyMYRhERkbi0qVLYBgGubm52LNnD8aPH6/EiOXH1NQUycnJiI+Ph1AoRGFhITZs2IBRo0bBx8cHxcXFSExMRF1dHbKysnDs2DGMGTNG2WHLTU5ODnr06CHRpu7b/DVvb+8Pbl8/Pz8cO3YMWVlZqKurQ2JiIl6+fAlvb28lR66mlDizgXUJCQmMvb0907VrV8bZ2VnixjAMs2/fPmbw4MFM165dmYEDBzJJSUlKjli+srOzmfHjxzMuLi5Mz549mVWrVjE1NTUMwzDMjRs3xMsGDhzI/Pjjj0qOVr6cnZ2Z8+fPv9Wurtv8zSlcDPPx7XvkyBHGx8eHcXZ2Zvz8/Jhr164pOmSNQVfhIoQQFql1uYAQQpSNkiwhhLCIkiwhhLCIkiwhhLCIkiwhhLCIkiwhhLCIkiwhhLCIkiwhhLCIkiwhhLCIkiwhhLCIkiwhhLCIkiwhhLDo/wC/c9FjcUDXaQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 400x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 400x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use(\"seaborn\")\n",
    " \n",
    "# 2. Generate a 10x10 random integer matrix\n",
    "data = [\n",
    "    [0, 5, 11, 5],\n",
    "    [0, 5, 23, 8],\n",
    "    [0, 11, 11, 8],\n",
    "    [0, 1, 6, 1]\n",
    "]\n",
    " \n",
    "# 3. Plot the heatmap\n",
    "plt.figure(figsize=(4,4))\n",
    "heat_map = sns.heatmap( data, linewidth = 1 , annot = True, xticklabels=[25, 50, 75, 100], yticklabels=['d 25', 'd 50', 'd 75', 'd 100'])\n",
    "plt.title(\"varying depth search succes rate\")\n",
    "plt.show()\n",
    "\n",
    "data = [\n",
    "    [0, 5, 9, 23],\n",
    "    [0, 1, 21, 25],\n",
    "    [0, 8, 13, 28],\n",
    "    [0, 14, 22, 8]\n",
    "]\n",
    " \n",
    "# 3. Plot the heatmap\n",
    "plt.figure(figsize=(4,4))\n",
    "heat_map = sns.heatmap( data, linewidth = 1 , annot = True, xticklabels=[25, 50, 75, 100], yticklabels=['s 50','s 200', 's 500', 's 1000'])\n",
    "plt.title(\"varying # MCTS sampling succes rate\")\n",
    "plt.show()\n",
    "\n",
    "data = [\n",
    "    [0, 0, 0, 10],\n",
    "    [0, 0, 0, 0],\n",
    "    [2, 9, 14, 16],\n",
    "    [0, 6, 17, 19],\n",
    "]\n",
    " \n",
    "# 3. Plot the heatmap\n",
    "plt.figure(figsize=(4,4))\n",
    "heat_map = sns.heatmap( data, linewidth = 1 , annot = True, xticklabels=[25, 50, 75, 100], yticklabels=['t 10', 't 50', 't 100', 't ada'])\n",
    "plt.title(\"varying training runs succes rate\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['0\\textpm0.0 &', '0\\textpm0.0 &', '0\\textpm0.0 &', '10\\textpm3.0 &']\n",
      "['0\\textpm0.0 &', '0\\textpm0.0 &', '0\\textpm0.0 &', '0\\textpm0.0 &']\n",
      "['2\\textpm1.4 &', '9\\textpm2.86 &', '14\\textpm3.47 &', '16\\textpm3.67 &']\n",
      "['0\\textpm0.0 &', '6\\textpm2.37 &', '17\\textpm3.76 &', '19\\textpm3.92 &']\n"
     ]
    }
   ],
   "source": [
    "X = [\n",
    "    [0,0,0,10],\n",
    "    [0,0,0,0],\n",
    "    [2,9,14,16],\n",
    "    [0,6,17,19]\n",
    "]\n",
    "for success_rates in X:\n",
    "    succes_std = []\n",
    "    for i in success_rates:\n",
    "        temp = np.zeros(100)\n",
    "        temp[:int(i)]=1\n",
    "        std = np.sqrt(100*np.var(temp))\n",
    "        succes_std.append(std)\n",
    "    x = [f'{success_rates[i]}\\textpm{round(succes_std[i], 2)} &' for i in range(len(success_rates))]\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(95.2941277890768, 98.7058722109232),\n",
       " (100.0, 100.0),\n",
       " (100.0, 100.0),\n",
       " (100.0, 100.0),\n",
       " (100.0, 100.0),\n",
       " (100.0, 100.0),\n",
       " (100.0, 100.0)]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "['2\\textpm1.4 &', '48\\textpm5.0 &', '47\\textpm4.99 &', '49\\textpm5.0 &']\n",
    "['83\\textpm3.76 &', '90\\textpm3.0 &', '90\\textpm3.0 &', '95\\textpm2.18 &']\n",
    "['51\\textpm5.0 &', '95\\textpm2.18 &', '93\\textpm2.55 &', '93\\textpm2.55 &']\n",
    "['75\\textpm4.33 &', '90\\textpm3.0 &', '95\\textpm2.18 &', '90\\textpm3.0 &']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.16"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "99202338b96e74e315910b3b20b9a2b0621872c4f26ff012cceee1c9c29d9125"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}