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In [1]:



    


import tensorflow as tf
import numpy as np
import gym
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam
from collections import deque



    


















    






Using TensorFlow backend.

















In [3]:



    


env = gym.make('CartPole-v0')



    


















    






[2017-07-12 23:06:44,506] Making new env: CartPole-v0

















In [4]:



    


state = env.reset()
for i in range(500):
    next_state, reward, done, _ = env.step(env.action_space.sample())
    print(reward)
    if done:
        print(i)
        break



    


















    






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In [19]:



    


lr = 0.001
mem_size = 2000
episodes = 1000
steps = 2000

epsilon = 1
epsilon_min = 0.01
epsilon_decay = 0.995
gamma = 0.95
batch_size = 32



    











In [20]:



    


bot = Sequential()
bot.add(Dense(units=24, input_dim=4, activation='relu'))
bot.add(Dense(units=24, activation='relu'))
bot.add(Dense(units=2, activation='linear'))
bot.compile(loss='mse', optimizer=Adam(lr))

memory = deque(maxlen=mem_size)



    











In [21]:



    


reward_list = []
for episode in range(episodes):
    state = env.reset()
    total_rewards = 0
    for step in range(steps):
        if epsilon >= np.random.rand():
            action = env.action_space.sample()
        else:
            action = np.argmax(bot.predict(np.reshape(state, (1, 4))))
        next_state, reward, done, _ = env.step(action)
        # reward = reward if not done else -10
        memory.append((state, action, next_state, reward, done))
        total_rewards = total_rewards + reward
        state = next_state
        if done:
            reward_list.append(total_rewards)
            print('episode:{} steps:{} total_reward:{} epsilon:{}'.format(episode+1, step+1, total_rewards, epsilon))
            break
    
    minibatch = [memory[ii] for ii in np.random.choice(range(len(memory)), batch_size)]
    for state, action, next_state, reward, done in minibatch:
        target = reward
        if not done:
            target = reward + gamma * np.amax(bot.predict(np.reshape(next_state, (1, 4))))
        target_act = bot.predict(np.reshape(state, (1, 4)))
        target_act[0][action] = target
        bot.fit( np.reshape(state, (1, 4)), target_act, epochs=1, verbose=0)
    if epsilon > epsilon_min:
        epsilon = epsilon * epsilon_decay



    


















    






episode:1 steps:20 total_reward:20.0 epsilon:1
episode:2 steps:11 total_reward:11.0 epsilon:0.995
episode:3 steps:89 total_reward:89.0 epsilon:0.990025
episode:4 steps:18 total_reward:18.0 epsilon:0.985074875
episode:5 steps:12 total_reward:12.0 epsilon:0.9801495006250001
episode:6 steps:26 total_reward:26.0 epsilon:0.9752487531218751
episode:7 steps:13 total_reward:13.0 epsilon:0.9703725093562657
episode:8 steps:24 total_reward:24.0 epsilon:0.9655206468094844
episode:9 steps:15 total_reward:15.0 epsilon:0.960693043575437
episode:10 steps:55 total_reward:55.0 epsilon:0.9558895783575597
episode:11 steps:13 total_reward:13.0 epsilon:0.9511101304657719
episode:12 steps:12 total_reward:12.0 epsilon:0.946354579813443
episode:13 steps:14 total_reward:14.0 epsilon:0.9416228069143757
episode:14 steps:18 total_reward:18.0 epsilon:0.9369146928798039
episode:15 steps:28 total_reward:28.0 epsilon:0.9322301194154049
episode:16 steps:13 total_reward:13.0 epsilon:0.9275689688183278
episode:17 steps:9 total_reward:9.0 epsilon:0.9229311239742362
episode:18 steps:14 total_reward:14.0 epsilon:0.918316468354365
episode:19 steps:19 total_reward:19.0 epsilon:0.9137248860125932
episode:20 steps:23 total_reward:23.0 epsilon:0.9091562615825302
episode:21 steps:17 total_reward:17.0 epsilon:0.9046104802746175
episode:22 steps:36 total_reward:36.0 epsilon:0.9000874278732445
episode:23 steps:19 total_reward:19.0 epsilon:0.8955869907338783
episode:24 steps:24 total_reward:24.0 epsilon:0.8911090557802088
episode:25 steps:17 total_reward:17.0 epsilon:0.8866535105013078
episode:26 steps:12 total_reward:12.0 epsilon:0.8822202429488013
episode:27 steps:19 total_reward:19.0 epsilon:0.8778091417340573
episode:28 steps:22 total_reward:22.0 epsilon:0.8734200960253871
episode:29 steps:22 total_reward:22.0 epsilon:0.8690529955452602
episode:30 steps:14 total_reward:14.0 epsilon:0.8647077305675338
episode:31 steps:26 total_reward:26.0 epsilon:0.8603841919146962
episode:32 steps:19 total_reward:19.0 epsilon:0.8560822709551227
episode:33 steps:38 total_reward:38.0 epsilon:0.851801859600347
episode:34 steps:19 total_reward:19.0 epsilon:0.8475428503023453
episode:35 steps:8 total_reward:8.0 epsilon:0.8433051360508336
episode:36 steps:26 total_reward:26.0 epsilon:0.8390886103705794
episode:37 steps:11 total_reward:11.0 epsilon:0.8348931673187264
episode:38 steps:17 total_reward:17.0 epsilon:0.8307187014821328
episode:39 steps:23 total_reward:23.0 epsilon:0.8265651079747222
episode:40 steps:15 total_reward:15.0 epsilon:0.8224322824348486
episode:41 steps:25 total_reward:25.0 epsilon:0.8183201210226743
episode:42 steps:14 total_reward:14.0 epsilon:0.8142285204175609
episode:43 steps:16 total_reward:16.0 epsilon:0.810157377815473
episode:44 steps:10 total_reward:10.0 epsilon:0.8061065909263957
episode:45 steps:23 total_reward:23.0 epsilon:0.8020760579717637
episode:46 steps:39 total_reward:39.0 epsilon:0.798065677681905
episode:47 steps:28 total_reward:28.0 epsilon:0.7940753492934954
episode:48 steps:16 total_reward:16.0 epsilon:0.7901049725470279
episode:49 steps:12 total_reward:12.0 epsilon:0.7861544476842928
episode:50 steps:15 total_reward:15.0 epsilon:0.7822236754458713
episode:51 steps:53 total_reward:53.0 epsilon:0.778312557068642
episode:52 steps:38 total_reward:38.0 epsilon:0.7744209942832988
episode:53 steps:20 total_reward:20.0 epsilon:0.7705488893118823
episode:54 steps:16 total_reward:16.0 epsilon:0.7666961448653229
episode:55 steps:11 total_reward:11.0 epsilon:0.7628626641409962
episode:56 steps:16 total_reward:16.0 epsilon:0.7590483508202912
episode:57 steps:19 total_reward:19.0 epsilon:0.7552531090661897
episode:58 steps:15 total_reward:15.0 epsilon:0.7514768435208588
episode:59 steps:20 total_reward:20.0 epsilon:0.7477194593032545
episode:60 steps:13 total_reward:13.0 epsilon:0.7439808620067382
episode:61 steps:22 total_reward:22.0 epsilon:0.7402609576967045
episode:62 steps:10 total_reward:10.0 epsilon:0.736559652908221
episode:63 steps:15 total_reward:15.0 epsilon:0.7328768546436799
episode:64 steps:15 total_reward:15.0 epsilon:0.7292124703704616
episode:65 steps:14 total_reward:14.0 epsilon:0.7255664080186093
episode:66 steps:11 total_reward:11.0 epsilon:0.7219385759785162
episode:67 steps:17 total_reward:17.0 epsilon:0.7183288830986236
episode:68 steps:14 total_reward:14.0 epsilon:0.7147372386831305
episode:69 steps:17 total_reward:17.0 epsilon:0.7111635524897149
episode:70 steps:12 total_reward:12.0 epsilon:0.7076077347272662
episode:71 steps:15 total_reward:15.0 epsilon:0.7040696960536299
episode:72 steps:18 total_reward:18.0 epsilon:0.7005493475733617
episode:73 steps:16 total_reward:16.0 epsilon:0.697046600835495
episode:74 steps:12 total_reward:12.0 epsilon:0.6935613678313175
episode:75 steps:11 total_reward:11.0 epsilon:0.6900935609921609
episode:76 steps:41 total_reward:41.0 epsilon:0.6866430931872001
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episode:86 steps:10 total_reward:10.0 epsilon:0.653073201944699
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episode:88 steps:18 total_reward:18.0 epsilon:0.6465587967553006
episode:89 steps:11 total_reward:11.0 epsilon:0.6433260027715241
episode:90 steps:17 total_reward:17.0 epsilon:0.6401093727576664
episode:91 steps:12 total_reward:12.0 epsilon:0.6369088258938781
episode:92 steps:12 total_reward:12.0 epsilon:0.6337242817644086
episode:93 steps:13 total_reward:13.0 epsilon:0.6305556603555866
episode:94 steps:23 total_reward:23.0 epsilon:0.6274028820538087
episode:95 steps:13 total_reward:13.0 epsilon:0.6242658676435396
episode:96 steps:25 total_reward:25.0 epsilon:0.6211445383053219
episode:97 steps:21 total_reward:21.0 epsilon:0.6180388156137953
episode:98 steps:16 total_reward:16.0 epsilon:0.6149486215357263
episode:99 steps:11 total_reward:11.0 epsilon:0.6118738784280476
episode:100 steps:17 total_reward:17.0 epsilon:0.6088145090359074
episode:101 steps:13 total_reward:13.0 epsilon:0.6057704364907278
episode:102 steps:14 total_reward:14.0 epsilon:0.6027415843082742
episode:103 steps:19 total_reward:19.0 epsilon:0.5997278763867329
episode:104 steps:14 total_reward:14.0 epsilon:0.5967292370047992
episode:105 steps:13 total_reward:13.0 epsilon:0.5937455908197752
episode:106 steps:12 total_reward:12.0 epsilon:0.5907768628656763
episode:107 steps:11 total_reward:11.0 epsilon:0.5878229785513479
episode:108 steps:11 total_reward:11.0 epsilon:0.5848838636585911
episode:109 steps:41 total_reward:41.0 epsilon:0.5819594443402982
episode:110 steps:14 total_reward:14.0 epsilon:0.5790496471185967
episode:111 steps:15 total_reward:15.0 epsilon:0.5761543988830038
episode:112 steps:11 total_reward:11.0 epsilon:0.5732736268885887
episode:113 steps:23 total_reward:23.0 epsilon:0.5704072587541458
episode:114 steps:70 total_reward:70.0 epsilon:0.567555222460375
episode:115 steps:38 total_reward:38.0 epsilon:0.5647174463480732
episode:116 steps:20 total_reward:20.0 epsilon:0.5618938591163328
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episode:118 steps:28 total_reward:28.0 epsilon:0.5562889678716474
episode:119 steps:12 total_reward:12.0 epsilon:0.5535075230322891
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episode:122 steps:16 total_reward:16.0 epsilon:0.5452463540625918
episode:123 steps:19 total_reward:19.0 epsilon:0.5425201222922789
episode:124 steps:12 total_reward:12.0 epsilon:0.5398075216808175
episode:125 steps:10 total_reward:10.0 epsilon:0.5371084840724134
episode:126 steps:13 total_reward:13.0 epsilon:0.5344229416520513
episode:127 steps:10 total_reward:10.0 epsilon:0.531750826943791
episode:128 steps:9 total_reward:9.0 epsilon:0.5290920728090721
episode:129 steps:21 total_reward:21.0 epsilon:0.5264466124450268
episode:130 steps:14 total_reward:14.0 epsilon:0.5238143793828016
episode:131 steps:13 total_reward:13.0 epsilon:0.5211953074858876
episode:132 steps:18 total_reward:18.0 epsilon:0.5185893309484582
episode:133 steps:22 total_reward:22.0 epsilon:0.5159963842937159
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episode:135 steps:42 total_reward:42.0 epsilon:0.510849320360386
episode:136 steps:61 total_reward:61.0 epsilon:0.5082950737585841
episode:137 steps:56 total_reward:56.0 epsilon:0.5057535983897912
episode:138 steps:51 total_reward:51.0 epsilon:0.5032248303978422
episode:139 steps:59 total_reward:59.0 epsilon:0.500708706245853
episode:140 steps:16 total_reward:16.0 epsilon:0.4982051627146237
episode:141 steps:18 total_reward:18.0 epsilon:0.49571413690105054
episode:142 steps:19 total_reward:19.0 epsilon:0.4932355662165453
episode:143 steps:22 total_reward:22.0 epsilon:0.4907693883854626
episode:144 steps:47 total_reward:47.0 epsilon:0.4883155414435353
episode:145 steps:73 total_reward:73.0 epsilon:0.4858739637363176
episode:146 steps:28 total_reward:28.0 epsilon:0.483444593917636
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In [22]:



    


import matplotlib.pyplot as plt
%matplotlib inline
def running_mean(x, N):
    cumsum = np.cumsum(np.insert(x, 0, 0)) 
    return (cumsum[N:] - cumsum[:-N]) / N 

smoothed_rews = running_mean(reward_list, 10)

plt.plot(reward_list, color='grey', alpha=0.3)
plt.plot(smoothed_rews)



    


















    
Out[22]:








[<matplotlib.lines.Line2D at 0x239e49e27b8>]










    
























In [28]:



    


state = env.reset()
total_reward = 0
for i in range(500):
    env.render()
    next_state, reward, done, _ = env.step(np.argmax(bot.predict(np.reshape(state, (1, 4)))))
    total_reward = total_reward + reward
    state = next_state
    if done:
        print(total_reward)
        env.close()
        break



    


















    






200.0

















In [ ]:

Content source: y2ee201/Deep-Learning-Nanodegree

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