From 89490e98ed89d0d39ed9d1ba36435c1954431f56 Mon Sep 17 00:00:00 2001 From: "i.h" Date: Wed, 24 Jan 2018 14:51:05 +0100 Subject: [PATCH 1/8] first commit --- .DS_Store | Bin 0 -> 6148 bytes Haloui/.DS_Store | Bin 0 -> 6148 bytes Haloui/FlappyAgent.py | 2 ++ run.py => Haloui/run.py | 0 4 files changed, 2 insertions(+) create mode 100644 .DS_Store create mode 100644 Haloui/.DS_Store create mode 100644 Haloui/FlappyAgent.py rename run.py => Haloui/run.py (100%) diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..b4e38f79ac1df99dbf2f21bbc928f848fd26cc5d GIT binary patch literal 6148 zcmeHKOG*Pl5UtW60XJEStG+@mAVV0zeJ-G6K!psGMP}b8@FY1;j^P=6ud5VB8#jVP zse)|FQUD;I(M6y$mtahCY#g&8ED*I&poOZZ7;53@ zPwv+^Wu0G@m*;Q#;t literal 0 HcmV?d00001 diff --git a/Haloui/.DS_Store b/Haloui/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..5008ddfcf53c02e82d7eee2e57c38e5672ef89f6 GIT binary patch literal 6148 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'next_pipe_dist_to_player': 283, 'next_pipe_top_y': 53, 'next_pipe_bottom_y': 153, 'next_next_pipe_dist_to_player': 427.0, 'next_next_pipe_top_y': 153, 'next_next_pipe_bottom_y': 253} + +def FlappyPolicy(state, screen,p): + action=None + print(p.act(action)) + if(np.random.randint(0,2)<1): + action=119 + return action + + diff --git a/Haloui/__pycache__/FlappyAgent.cpython-36.pyc b/Haloui/__pycache__/FlappyAgent.cpython-36.pyc new file mode 100644 index 0000000000000000000000000000000000000000..cddc7300037be92361ceb963c8fa1caac82710f4 GIT binary patch literal 383 zcmYjN!Ait15S=vb?y|V6s2^cL>_PA(qA21)7E!$P7Lu^GrfEr1q|iOBKO_E#-;k@P z^%Fcfsh17Rn|XPYH%v}vv+3tgk$*)1->`L%!zG42L=Yf|f}Ds*(09lqL7pIuH>kjZ z(~$7Q7@@w#u*V1jR@6Zsm87R@vL*yvRMIjQ=(9$rS1?B+UeTVSTe2_&-)zF%%}~8z zl_oxEOrf2pj5{Cc#*YlsqOSZnWK`1FC)P11{m617rS=4yu)C=B2bej!{GMgcRvMdC zjAu7BZ!4*t%^vUh3sXw!rOfV_s@o#FRjg^ct0n3uP3L$1`9rM=-i42_Qv@I}nUj4) lvxh9DKGAK}bY_ffxEiKz!7d7Za$eP9oZ(sQTvwL>W+4s#k_x5ykbxn75rLOK|dmB3$sligbu17+G 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training flappy bird how to fly +It is a Deep Q-Network train by "screen" variable +To achieve the level obtained with the attached DQN (flappy_brain), +We needed close to 160,000 frames to perform the training, it has been done +using google cloud engine + + +@author: Ilyass_Haloui +""" +from ple.games.flappybird import FlappyBird +from ple import PLE +import numpy as np +from collections import deque +from keras.models import Sequential +from keras.layers import Dense, Conv2D, Flatten +from keras.optimizers import Adam +from skimage.color import rgb2gray +from skimage.transform import resize + + +def createNetwork(): + #Create the CNN with Adam Opt + DQNF = Sequential() + DQNF.add(Conv2D(filters=16, kernel_size=(8,8), strides=4, activation="relu", input_shape=(80,80,4))) + DQNF.add(Conv2D(filters=32, kernel_size=(4,4), strides=2,activation="relu")) + DQNF.add(Flatten()) + DQNF.add(Dense(units=256, activation="relu")) + DQNF.add(Dense(units=2, activation="linear")) + DQNF.compile(optimizer=Adam(lr=1e-4), loss='mean_squared_error') + print(DQNF.summary()) + return DQNF + + +# epsilon=1 on the first 5k steps and then linearly from 0.1 to 0.001 +def epsilon(step): + if step < 5e3: + return 1 + elif step < 1e6: + return (0.1 - 5e3*(1e-3-0.1)/(1e6-5e3)) + step * (1e-3-0.1)/(1e6-5e3) + else: + return 1e-3 + +#We cut screen edges in order to simplify the training +def process_screen(screen): + return 255*resize(rgb2gray(screen[60:, 25:310,:]),(80,80)) + +# There is no negative reward ! +def clip_reward(r): + if r!=1: + rr=0.1 + else: + rr=r + return rr + +def greedy_action(network, x): + Q = network.predict(np.array([x])) + return np.argmax(Q) + +def policy_eval(p, games, network): + """ + Monte carlo evaluation of the mean score and max score of a 10000 frame episode + """ + list_actions = p.getActionSet() + cumulated = np.zeros((games)) + for i in range(games): + stackedframes = deque([np.zeros((80,80)),np.zeros((80,80)),np.zeros((80,80)),np.zeros((80,80))], maxlen=4) + p.reset_game() + while(not p.game_over()): + screen = process_screen(p.getScreenRGB()) + stackedframes.append(screen) + frameStacked = np.stack(stackedframes, axis=-1) + action = list_actions[np.argmax(network.predict(np.expand_dims(frameStacked,axis=0)))] + reward = p.act(action) + cumulated[i] += reward + mean_score = np.mean(cumulated) + max_score = np.max(cumulated) + return mean_score, max_score + +class MemoryBuffer: + "An experience replay buffer using numpy arrays" + def __init__(self, length, screen_shape, action_shape): + self.length = length + self.screen_shape = screen_shape + self.action_shape = action_shape + shape = (length,) + screen_shape + self.screens_x = np.zeros(shape, dtype=np.uint8) # starting states + self.screens_y = np.zeros(shape, dtype=np.uint8) # resulting states + shape = (length,) + action_shape + self.actions = np.zeros(shape, dtype=np.uint8) # actions + self.rewards = np.zeros((length,1), dtype=np.uint8) # rewards + self.terminals = np.zeros((length,1), dtype=np.bool) # true if resulting state is terminal + self.terminals[-1] = True + self.index = 0 # points one position past the last inserted element + self.size = 0 # current size of the buffer + + def append(self, screenx, a, r, screeny, d): + self.screens_x[self.index] = screenx + self.actions[self.index] = a + self.rewards[self.index] = r + self.screens_y[self.index] = screeny + self.terminals[self.index] = d + self.index = (self.index+1) % self.length + self.size = np.min([self.size+1,self.length]) + + def stacked_frames_x(self, index): + im_deque = deque(maxlen=4) + pos = index % self.length + for i in range(4): + im = self.screens_x[pos] + im_deque.appendleft(im) + test_pos = (pos-1) % self.length + if self.terminals[test_pos] == False: + pos = test_pos + return np.stack(im_deque, axis=-1) + + def stacked_frames_y(self, index): + im_deque = deque(maxlen=4) + pos = index % self.length + for i in range(4): + im = self.screens_y[pos] + im_deque.appendleft(im) + test_pos = (pos-1) % self.length + if self.terminals[test_pos] == False: + pos = test_pos + return np.stack(im_deque, axis=-1) + + def minibatch(self, size): + indices = np.random.choice(self.size, size=size, replace=False) + x = np.zeros((size,)+self.screen_shape+(4,)) + y = np.zeros((size,)+self.screen_shape+(4,)) + for i in range(size): + x[i] = self.stacked_frames_x(indices[i]) + y[i] = self.stacked_frames_y(indices[i]) + return x, self.actions[indices], self.rewards[indices], y, self.terminals[indices] + + + +total_steps = 300000 +replay_memory_size = 300000 +mini_batch_size = 32 +gamma = 0.99 +eval_period = 10000 +nb_epochs = total_steps // eval_period +epoch=-1 +stop_training = False + + +DQNF = createNetwork() + + +game = FlappyBird(graphics="fixed") +p = PLE(game, fps=30, frame_skip=1, num_steps=1, force_fps=True, + display_screen=False) + +list_actions = p.getActionSet() + +p.init() +p.reset_game() + +screen_x = process_screen(p.getScreenRGB()) +stacked_x = deque([screen_x, screen_x, screen_x, screen_x], maxlen=4) +x = np.stack(stacked_x, axis=-1) + +replay_memory = MemoryBuffer(replay_memory_size, screen_x.shape, (1,)) + +mean_score = np.zeros((nb_epochs)) +max_score = np.zeros((nb_epochs)) + + +for step in range(total_steps): + + + # Score evaluation: + if(step%eval_period == 0 and step>0): + epoch += 1 + print(f"[ Epoch - periode ] :{ [ (epoch+1) , eval_period] }; ") + print('Starting score evaluation.. : ') + DQNF.save('flappy_brain.h5') + nb_games = 100 + mean_score[epoch], max_score[epoch] = policy_eval(p, nb_games, DQNF) + print('Score : {}/{} (mean/max)'.format(mean_score[epoch],max_score[epoch])) + print('Score eval done..') + + + + if (mean_score[epoch-1] > 30): + stop_training = True + + + + if not stop_training: + if np.random.rand() < epsilon(step): + a = np.random.randint(0,2) + else: + a = greedy_action(DQNF, x) + + + + r = clip_reward(p.act(list_actions[a])) + + + screen_y = process_screen(p.getScreenRGB()) + + + replay_memory.append(screen_x, a, r, screen_y, p.game_over()) + + + # train + if (step > mini_batch_size and step > 10000): + X,A,R,Y,D = replay_memory.minibatch(mini_batch_size) + QY = DQNF.predict(Y) + QYmax = QY.max(1).reshape((mini_batch_size,1)) + update = R + gamma * (1-D) * QYmax + QX = DQNF.predict(X) + QX[np.arange(mini_batch_size), A.ravel()] = update.ravel() + DQNF.train_on_batch(x=X, y=QX) + # Save regularly: + if (step > 0 and step % 2500 == 0): + DQNF.save('flappy_brain.h5') + + #if not terminal stage + if p.game_over()==True: + # restart episode + p.reset_game() + screen_x = process_screen(p.getScreenRGB()) + stacked_x = deque([screen_x, screen_x, screen_x, screen_x], maxlen=4) + x = np.stack(stacked_x, axis=-1) + else: + # keep going + screen_x = screen_y + stacked_x.append(screen_x) + x = np.stack(stacked_x, axis=-1) + + + if stop_training: + break + +DQNF.save('flappy_brain_2.h5') + + +print("Training completed") + + + diff --git a/Haloui/run.py b/Haloui/run.py old mode 100644 new mode 100755 index 4c1c0ae..ab4ea9c --- a/Haloui/run.py +++ b/Haloui/run.py @@ -2,10 +2,11 @@ from ple.games.flappybird import FlappyBird from ple import PLE import numpy as np -import FlappyPolicy +from FlappyAgent import FlappyPolicy -game = FlappyBird() +game = FlappyBird(graphics="fixed") # use "fancy" for full background, random bird color and random pipe color, use "fixed" (default) for black background and constant bird and pipe colors. p = PLE(game, fps=30, frame_skip=1, num_steps=1, force_fps=False, display_screen=True) +# Note: if you want to see you agent act in real time, set force_fps to False. But don't use this setting for learning, just for display purposes. p.init() reward = 0.0 @@ -23,6 +24,6 @@ reward = p.act(action) cumulated[i] = cumulated[i] + reward - + average_score = np.mean(cumulated) max_score = np.max(cumulated) From fd7db4f1d6e0bc9dc518c370be04fada4c2a02e9 Mon Sep 17 00:00:00 2001 From: "i.h" Date: Fri, 9 Mar 2018 23:55:58 +0100 Subject: [PATCH 3/8] commit_flappy_screen --- .DS_Store | Bin 8196 -> 8196 bytes Haloui/FlappyAgent.py | 58 +++++++++++++++++++++++++++--------------- 2 files changed, 38 insertions(+), 20 deletions(-) mode change 100644 => 100755 Haloui/FlappyAgent.py diff --git a/.DS_Store b/.DS_Store index d36d77c66b00bb6a745b65638bfadf3d25e52be3..ace66b8ad5561820d63c919e67e9dbf69ca08125 100644 GIT binary patch delta 32 mcmZp1XmOa}&&atkU^hP_=Vl&(18f^htQj}6OB@9_2 delta 54 ycmZp1XmOa}&&ahgU^hP_*Jd7p18kfu3?&Sy48;s3oA-(tF>Yp;IEpH!&j Date: Fri, 9 Mar 2018 23:58:51 +0100 Subject: [PATCH 4/8] last_commit_flappy_screen --- Haloui/FlappyAgent.py | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/Haloui/FlappyAgent.py b/Haloui/FlappyAgent.py index 57c058a..b477522 100755 --- a/Haloui/FlappyAgent.py +++ b/Haloui/FlappyAgent.py @@ -1,6 +1,5 @@ import numpy as np from keras.models import load_model -#from challenge_utils import process_screen from collections import deque from ple.games.flappybird import FlappyBird from ple import PLE @@ -18,7 +17,6 @@ p = PLE(game, fps=30, frame_skip=1, num_steps=1) list_actions = p.getActionSet() -size_img = (80,80) DequeFX = deque([np.zeros(size_img),np.zeros(size_img),np.zeros(size_img),np.zeros(size_img)], maxlen=4) @@ -35,11 +33,11 @@ def FlappyPolicy(state, screen): # If new game, build new stacked frames if not np.any(x[10:,:]): # to know if x is the initial flappy position - DequeFX = deque([np.zeros(size_img),np.zeros(size_img),np.zeros(size_img),np.zeros(size_img)], maxlen=4) + DequeFX = deque([np.zeros((80,80)),np.zeros((80,80)),np.zeros((80,80)),np.zeros((80,80))], maxlen=4) #else: DequeFX.append(x) FramesFX = np.stack(DequeFX, axis=-1) - a = list_actions[np.argmax(DQN.predict(np.expand_dims(FramesFX,axis=0)))] #10 times quicker than np.array([]) + act = list_actions[np.argmax(DQN.predict(np.expand_dims(FramesFX,axis=0)))] - return a # Return the action to perform \ No newline at end of file + return act # Return the action to perform From 048e966245a854bf9d22b70c02e5b31b421fe69d Mon Sep 17 00:00:00 2001 From: ilyassh Date: Sat, 10 Mar 2018 01:54:21 +0100 Subject: [PATCH 5/8] Update FlappyAgent.py --- Haloui/FlappyAgent.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Haloui/FlappyAgent.py b/Haloui/FlappyAgent.py index b477522..1056399 100755 --- a/Haloui/FlappyAgent.py +++ b/Haloui/FlappyAgent.py @@ -18,7 +18,7 @@ list_actions = p.getActionSet() -DequeFX = deque([np.zeros(size_img),np.zeros(size_img),np.zeros(size_img),np.zeros(size_img)], maxlen=4) +DequeFX = deque([np.zeros((80,80)),np.zeros((80,80)),np.zeros((80,80)),np.zeros((80,80))], maxlen=4) def process_screen(screen): return 255*resize(rgb2gray(screen[60:, 25:310,:]),(80,80)) From 92770407ba6927c64cd2d9419d5028194cd63948 Mon Sep 17 00:00:00 2001 From: "i.h" Date: Sat, 10 Mar 2018 01:56:45 +0100 Subject: [PATCH 6/8] commit_final_flappy_screen --- Haloui/FlappyAgent.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/Haloui/FlappyAgent.py b/Haloui/FlappyAgent.py index b477522..e0f2cb2 100755 --- a/Haloui/FlappyAgent.py +++ b/Haloui/FlappyAgent.py @@ -18,7 +18,7 @@ list_actions = p.getActionSet() -DequeFX = deque([np.zeros(size_img),np.zeros(size_img),np.zeros(size_img),np.zeros(size_img)], maxlen=4) +DequeFX = deque([np.zeros((80,80)),np.zeros((80,80)),np.zeros((80,80)),np.zeros((80,80))], maxlen=4) def process_screen(screen): return 255*resize(rgb2gray(screen[60:, 25:310,:]),(80,80)) @@ -40,4 +40,4 @@ def FlappyPolicy(state, screen): FramesFX = np.stack(DequeFX, axis=-1) act = list_actions[np.argmax(DQN.predict(np.expand_dims(FramesFX,axis=0)))] - return act # Return the action to perform + return act From cc387b2efa3fbedf5a09c9322e8dedf1007dac02 Mon Sep 17 00:00:00 2001 From: "i.h" Date: Sat, 10 Mar 2018 10:47:36 +0100 Subject: [PATCH 7/8] commit-final --- Haloui/FlappyAgent.py | 9 ++------- 1 file changed, 2 insertions(+), 7 deletions(-) diff --git a/Haloui/FlappyAgent.py b/Haloui/FlappyAgent.py index e0f2cb2..66b34a6 100755 --- a/Haloui/FlappyAgent.py +++ b/Haloui/FlappyAgent.py @@ -31,13 +31,8 @@ def FlappyPolicy(state, screen): x = process_screen(screen) - # If new game, build new stacked frames - if not np.any(x[10:,:]): # to know if x is the initial flappy position - DequeFX = deque([np.zeros((80,80)),np.zeros((80,80)),np.zeros((80,80)),np.zeros((80,80))], maxlen=4) - - #else: DequeFX.append(x) - FramesFX = np.stack(DequeFX, axis=-1) - act = list_actions[np.argmax(DQN.predict(np.expand_dims(FramesFX,axis=0)))] + FramesFX = np.stack( DequeFX , axis=-1) + act = list_actions[ np.argmax( DQN.predict( np.expand_dims(FramesFX,axis=0) ) ) ] return act From d5bf874173fd649d8bab8c0c48e3e65e904093eb Mon Sep 17 00:00:00 2001 From: ilyassh Date: Sat, 10 Mar 2018 10:51:12 +0100 Subject: [PATCH 8/8] Update flappy_ilyass.py --- Haloui/flappy_ilyass.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/Haloui/flappy_ilyass.py b/Haloui/flappy_ilyass.py index 622105e..32c9101 100644 --- a/Haloui/flappy_ilyass.py +++ b/Haloui/flappy_ilyass.py @@ -3,11 +3,11 @@ """ Created on Fri Mar 9 10:15:16 2018 -This is the code for training flappy bird how to fly -It is a Deep Q-Network train by "screen" variable +This is the code for training flappy , it has to learn bird how to fly through pipes. +It is a Deep Q-Network trained by "screen" variable To achieve the level obtained with the attached DQN (flappy_brain), We needed close to 160,000 frames to perform the training, it has been done -using google cloud engine +using google cloud engine in about 3 hours (16 GB CPU only) @author: Ilyass_Haloui @@ -36,7 +36,7 @@ def createNetwork(): return DQNF -# epsilon=1 on the first 5k steps and then linearly from 0.1 to 0.001 +# epsilon=1 on the first 5000 steps and then decreases step by step from 0.1 to 0.001 def epsilon(step): if step < 5e3: return 1

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