Site updated: 2024-12-19 15:06:03

2018/10/29/二次入坑raspberry-pi/requirements.txt

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+absl-py==0.3.0
+astor==0.7.1
+autopep8==1.3.5
+backcall==0.1.0
+bleach==2.1.4
+certifi==2018.8.24
+chardet==3.0.4
+colorama==0.3.9
+cycler==0.10.0
+decorator==4.3.0
+defusedxml==0.5.0
+entrypoints==0.2.3
+gast==0.2.0
+grpcio==1.14.1
+html5lib==1.0.1
+idna==2.7
+ipykernel==5.0.0
+ipython==7.0.1
+ipython-genutils==0.2.0
+ipywidgets==7.4.2
+isort==4.3.4
+jedi==0.12.1
+Jinja2==2.10
+jsonschema==2.6.0
+jupyter==1.0.0
+jupyter-client==5.2.3
+jupyter-console==5.2.0
+jupyter-core==4.4.0
+kiwisolver==1.0.1
+lxml==4.2.5
+Markdown==2.6.11
+MarkupSafe==1.0
+matplotlib==2.2.2
+mccabe==0.6.1
+mistune==0.8.3
+nbconvert==5.4.0
+nbformat==4.4.0
+nltk==3.3
+notebook==5.7.0
+numpy==1.14.5
+opencv-python==3.4.2.17
+pandas==0.23.4
+pandas-datareader==0.7.0
+pandocfilters==1.4.2
+parso==0.3.1
+pickleshare==0.7.5
+Pillow==5.2.0
+prometheus-client==0.3.1
+prompt-toolkit==1.0.15
+protobuf==3.6.0
+pycodestyle==2.4.0
+Pygments==2.2.0
+pyparsing==2.2.0
+python-dateutil==2.7.3
+pytz==2018.5
+pywinpty==0.5.4
+pyzmq==17.1.2
+qtconsole==4.4.1
+requests==2.19.1
+scikit-learn==0.19.2
+scipy==1.1.0
+Send2Trash==1.5.0
+simplegeneric==0.8.1
+six==1.11.0
+tensorboard==1.10.0
+tensorboardX==1.4
+tensorflow==1.10.0
+termcolor==1.1.0
+terminado==0.8.1
+testpath==0.4.1
+torch==0.4.1
+torchfile==0.1.0
+torchnet==0.0.4
+torchvision==0.2.1
+tornado==5.1.1
+traitlets==4.3.2
+urllib3==1.23
+visdom==0.1.8.5
+wcwidth==0.1.7
+webencodings==0.5.1
+websocket-client==0.53.0
+Werkzeug==0.14.1
+widgetsnbextension==3.4.2
+wrapt==1.10.11
+xgboost==0.80

2023/03/11/这是一份给算法同学的强化学习入门材料/a2c.py

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+import os
+import gym
+import numpy as np
+from copy import deepcopy
+from itertools import chain
+from collections import deque
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.distributions import Categorical
+
+env = gym.make('CartPole-v1')
+env = env.unwrapped
+state_number = env.observation_space.shape[0]
+action_number = env.action_space.n
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+class Actor(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+        self.layers = nn.Sequential(
+            nn.Linear(state_number, 32),
+            nn.ReLU(inplace=True),
+            nn.Linear(32, 32),
+            nn.ReLU(inplace=True),
+            nn.Linear(32, action_number),
+            nn.Softmax(dim=-1),
+        )
+
+    def forward(self, state):
+        pi = self.layers(state)  # (batch_size, action_number)
+        return pi
+
+class Critic(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+        self.layers = nn.Sequential(
+            nn.Linear(state_number, 32),
+            nn.ReLU(inplace=True),
+            nn.Linear(32, 32),
+            nn.ReLU(inplace=True),
+            nn.Linear(32, 1),
+        )
+
+    def forward(self, state):
+        value = self.layers(state).squeeze(-1)  # (batch_size,)
+        return value
+
+class ActorCritic():
+
+    def __init__(
+        self,
+        gamma=0.99,
+        update_steps=1,
+        lr=5e-4,
+        weight_decay=0.0,
+    ):
+        self.gamma = gamma
+        self.update_steps = update_steps
+
+        self.buffer = []
+        self.actor = Actor().to(device)
+        self.critic = Critic().to(device)
+        self.optimizer = torch.optim.Adam(
+            chain(self.actor.parameters(), self.critic.parameters()), 
+            lr=lr, weight_decay=weight_decay
+        )
+        self.loss_fct = nn.SmoothL1Loss()
+
+    @torch.no_grad()
+    def choose_action(self, state):
+        state = torch.from_numpy(state).float().unsqueeze(0).to(device)
+        pi = self.actor(state)
+        dist = torch.distributions.Categorical(pi)
+        action = dist.sample().item()
+        return action
+
+    @torch.no_grad()
+    def get_value(self, state):
+        state = torch.from_numpy(state).float().unsqueeze(0).to(device)
+        value = self.critic(state)
+        return value
+
+    def store_experience(self, experience):
+        self.buffer.append(experience)
+
+    def update(self):
+        # 得到数据
+        get_tensor = lambda x: torch.tensor([b[x] for b in self.buffer]).to(device)
+        states = get_tensor(0).float()
+        actions = get_tensor(1).long()
+        rewards = get_tensor(2).float()
+        next_states = get_tensor(3).float()
+        done = get_tensor(4).long()
+
+        # # 改进2：为每步t赋予不同权重
+        # for t in reversed(range(0, rewards.size(0) - 1)):
+        #     rewards[t] = rewards[t] + self.gamma * rewards[t + 1]
+        # 改进1：增加一个奖励基准$b$，这里用均值；另归一化，有助于收敛
+        rewards = (rewards - rewards.mean()) / rewards.std()
+
+        # 计算target
+        with torch.no_grad():
+            # 动作价值函数 Q^{\pi}(s, a) = r(s, a) + \gamma \sum_{s' \in S} P(s'|s, a) V^{\pi}(s')
+            target_v = rewards + self.gamma * self.critic(next_states)
+            # 优势函数 A^{\pi}(s, a) = Q^{\pi}(s, a) - V^{\pi}(s)
+            advantage = target_v - self.critic(states)
+
+        for i in range(self.update_steps):
+            # 计算损失
+            pi = self.actor(states)
+            action_log_probs = torch.sum(pi.log() * F.one_hot(actions), dim=1)
+
+            loss_actor = - (action_log_probs * advantage).mean()    # 基于TD误差
+
+            value = self.critic(states)
+            loss_critic = self.loss_fct(value, target_v)
+
+            loss = loss_actor + loss_critic
+            self.optimizer.zero_grad()
+            loss.backward()
+            self.optimizer.step()
+
+        # 清除缓存
+        del self.buffer[:]
+
+        return loss.item()
+
+def train(agent, num_episodes=5000, render=False):
+    step = 0
+    for i in range(num_episodes):
+        total_rewards = 0
+        done = False
+        state, _ = env.reset()
+        while not done:
+            step += 1
+            if render: env.render()
+            # 选择动作
+            action = agent.choose_action(state)
+            # 与环境产生交互
+            next_state, reward, done, truncated, info = env.step(action)
+            # 预处理，修改reward，你也可以不修改奖励，直接用reward，都能收敛
+            x, x_dot, theta, theta_dot = next_state
+            r1 = (env.x_threshold - abs(x)) / env.x_threshold - 0.8
+            r2 = (env.theta_threshold_radians - abs(theta)) / env.theta_threshold_radians - 0.5
+            r3 = 3 * r1 + r2
+            # 经验缓存
+            agent.store_experience((state, action, r3, next_state, done))
+            # 更新状态
+            state = next_state
+            total_rewards += reward
+
+        # 回合结束，更新参数
+        loss = agent.update()
+        if i % 50 == 0:
+            print('episode:{} reward:{}'.format(i, total_rewards))
+
+def test(agent, num_episodes=10, render=False):
+    env = gym.make('CartPole-v1', render_mode="human" if render else None)
+    step = 0
+    eval_rewards = []
+    for i in range(num_episodes):
+        total_rewards = 0
+        done = False
+        state, _ = env.reset()
+        while not done:
+            step += 1
+            if render: env.render()
+            # 选择动作
+            action = agent.choose_action(state)
+            # 与环境产生交互
+            next_state, reward, done, truncated, info = env.step(action)
+            # 更新状态
+            state = next_state
+            total_rewards += reward
+        eval_rewards.append(total_rewards)
+    return sum(eval_rewards) / len(eval_rewards)
+
+if __name__ == "__main__":
+    agent = ActorCritic()
+    train(agent, render=False)
+    test(agent, render=True)