【強化学習】自前ゲームをdqn, duel-dqn, sarsa,そしてcemで学習してみる♪

dqn第四夜は、とにかくExampleのCartPoleと自前ゲームの沼のサイズを変えて、dqn, duel-dqn, sarsa,そしてcemで学習してみた。

```dqn_CartPole_16.py
 49974/50000: episode: 306, duration: 0.964s, episode steps: 200, steps per second: 208, episode reward: 200.000, mean reward: 1.000 [1.000, 1.000], mean action: 0.505 [0.000, 1.000], mean observation: 0.145 [-0.828, 0.976], loss: 9.324725, mean_absolute_error: 48.265038, mean_q: 96.748772
done, took 243.378 seconds
Testing for 5 episodes ...
Episode 1: reward: 185.000, steps: 185
Episode 2: reward: 200.000, steps: 200
Episode 3: reward: 162.000, steps: 162
Episode 4: reward: 200.000, steps: 200
Episode 5: reward: 167.000, steps: 167
```

```duel_avg_dqn_CartPole_16.py
 49898/50000: episode: 285, duration: 1.004s, episode steps: 200, steps per second: 199, episode reward: 200.000, mean reward: 1.000 [1.000, 1.000], mean action: 0.495 [0.000, 1.000], mean observation: -0.044 [-1.410, 1.040], loss: 13.267376, mean_absolute_error: 42.688183, mean_q: 85.324562
done, took 257.371 seconds
Testing for 5 episodes ...
Episode 1: reward: 200.000, steps: 200
Episode 2: reward: 200.000, steps: 200
Episode 3: reward: 200.000, steps: 200
Episode 4: reward: 200.000, steps: 200
Episode 5: reward: 200.000, steps: 200
```

```duel_max_dqn_CartPole_16.py
 49986/50000: episode: 299, duration: 1.009s, episode steps: 200, steps per second: 198, episode reward: 200.000, mean reward: 1.000 [1.000, 1.000], mean action: 0.505 [0.000, 1.000], mean observation: 0.001 [-0.673, 0.823], loss: 8.209622, mean_absolute_error: 43.265770, mean_q: 86.812187
done, took 257.197 seconds
Testing for 5 episodes ...
Episode 1: reward: 200.000, steps: 200
Episode 2: reward: 200.000, steps: 200
Episode 3: reward: 200.000, steps: 200
Episode 4: reward: 200.000, steps: 200
Episode 5: reward: 200.000, steps: 200
```

```duel_naive_dqn_CartPole_16.py
 49980/50000: episode: 290, duration: 0.989s, episode steps: 200, steps per second: 202, episode reward: 200.000, mean reward: 1.000 [1.000, 1.000], mean action: 0.500 [0.000, 1.000], mean observation: -0.036 [-1.237, 1.350], loss: 8.542970, mean_absolute_error: 41.422867, mean_q: 83.247711
done, took 251.230 seconds
Testing for 5 episodes ...
Episode 1: reward: 200.000, steps: 200
Episode 2: reward: 200.000, steps: 200
Episode 3: reward: 200.000, steps: 200
Episode 4: reward: 200.000, steps: 200
Episode 5: reward: 200.000, steps: 200
```

```sarsa_CartPole_16.py
 49932/50000: episode: 699, duration: 0.751s, episode steps: 200, steps per second: 266, episode reward: 200.000, mean reward: 1.000 [1.000, 1.000], mean action: 0.495 [0.000, 1.000], mean observation: -0.051 [-1.362, 0.997], loss: 13.942631, mean_absolute_error: 34.059151, mean_q: 67.622353
done, took 190.890 seconds
Testing for 5 episodes ...
Episode 1: reward: 143.000, steps: 143
Episode 2: reward: 145.000, steps: 145
Episode 3: reward: 124.000, steps: 124
Episode 4: reward: 142.000, steps: 142
Episode 5: reward: 142.000, steps: 142
```

```cem_CartPole_256.py
 99983/100000: episode: 1989, duration: 0.144s, episode steps: 174, steps per second: 1212, episode reward: 174.000, mean reward: 1.000 [1.000, 1.000], mean action: 0.489 [0.000, 1.000], mean observation: -0.052 [-1.114, 1.069], mean_best_reward: --
done, took 94.657 seconds
Testing for 5 episodes ...
Episode 1: reward: 159.000, steps: 159
Episode 2: reward: 152.000, steps: 152
Episode 3: reward: 140.000, steps: 140
Episode 4: reward: 200.000, steps: 200
Episode 5: reward: 110.000, steps: 110
```

###自前ゲーム＿沼

```py
class MyEnv(gym.Env):
    metadata = {'render.modes': ['human', 'ansi']}
    FIELD_TYPES = [
        'S',  # 0: スタート
        'G',  # 1: ゴール
        '~',  # 2: 芝生(敵の現れる確率1/10)
        'w',  # 3: 森(敵の現れる確率1/2→3/4)
        '=',  # 4: 毒沼(1step毎に1のダメージ, 敵の現れる確率1/2)
        'A',  # 5: 山(歩けない)
        'Y',  # 6: 勇者
    ]
    MAP = np.array([
        [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],  # "AAAAAAAAAAAA"
        [5, 5, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],  # "AA~~~~~~~~~~"
        [5, 5, 2, 0, 2, 2, 5, 2, 2, 4, 2, 2],  # "AA~S~~A~~=~~"
        [5, 2, 2, 2, 2, 2, 5, 5, 4, 4, 2, 2],  # "A~~~~~AA==~~"
        [2, 2, 3, 3, 3, 3, 5, 5, 2, 2, 3, 3],  # "~~wwwwAA~~ww"
        [2, 3, 3, 3, 3, 5, 2, 1, 2, 2, 2, 3],  # "~wwwwA~G~~~w"
        [2, 2, 2, 2, 2, 2, 2, 4, 4, 4, 4, 4],  # "~~~~~~~====="
    ])
    MAX_STEPS = 100
```

```dqn_numa84.py
 99994/100000: episode: 5806, duration: 0.090s, episode steps: 18, steps per second: 199, episode reward: 30.000, mean reward: 1.667 [-1.000, 47.000], mean action: 1.167 [0.000, 3.000], mean observation: 3.151 [0.000, 6.000], loss: 7.566477, mean_absolute_error: 50.581699, mean_q: 69.552818
done, took 522.202 seconds
Testing for 5 episodes ...
Episode 1: reward: 59.000, steps: 11
Episode 2: reward: 79.000, steps: 11
Episode 3: reward: 59.000, steps: 11
Episode 4: reward: 89.000, steps: 11
Episode 5: reward: 79.000, steps: 11
```

```dqn_numa_duel_avg84.py
 99991/100000: episode: 5892, duration: 0.106s, episode steps: 18, steps per second: 169, episode reward: 72.000, mean reward: 4.000 [-1.000, 89.000], mean action: 0.889 [0.000, 3.000], mean observation: 3.154 [0.000, 6.000], loss: 9.651028, mean_absolute_error: 49.922932, mean_q: 69.437813
done, took 613.675 seconds
Testing for 5 episodes ...
Episode 1: reward: 59.000, steps: 11
Episode 2: reward: 69.000, steps: 11
Episode 3: reward: 59.000, steps: 11
Episode 4: reward: 89.000, steps: 11
Episode 5: reward: 69.000, steps: 11
```

```dqn_numa_duel_naive84.py
 99989/100000: episode: 6925, duration: 0.085s, episode steps: 14, steps per second: 165, episode reward: 47.000, mean reward: 3.357 [-1.000, 60.000], mean action: 1.143 [0.000, 3.000], mean observation: 3.153 [0.000, 6.000], loss: 10.629865, mean_absolute_error: 44.408119, mean_q: 61.224575
done, took 586.748 seconds
Testing for 5 episodes ...
Episode 1: reward: 82.000, steps: 9
Episode 2: reward: 62.000, steps: 9
Episode 3: reward: 62.000, steps: 9
Episode 4: reward: 72.000, steps: 9
Episode 5: reward: 72.000, steps: 9
```

```py
class MyEnv(gym.Env):
    metadata = {'render.modes': ['human', 'ansi']}
    FIELD_TYPES = [
        'S',  # 0: スタート
        'G',  # 1: ゴール
        '~',  # 2: 芝生(敵の現れる確率1/10)
        'w',  # 3: 森(敵の現れる確率1/2→3/4)
        '=',  # 4: 毒沼(1step毎に1のダメージ, 敵の現れる確率1/2)
        'A',  # 5: 山(歩けない)
        'Y',  # 6: 勇者
    ]
    MAP = np.array([
        [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],  # "AAAAAAAAAAAA"
        [5, 5, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],  # "AA~~~~~~~~~~"
        [5, 5, 2, 0, 2, 2, 5, 2, 2, 4, 2, 2],  # "AA~S~~A~~=~~"
        [5, 2, 2, 2, 2, 2, 5, 5, 4, 4, 2, 2],  # "A~~~~~AA==~~"
        [2, 2, 3, 3, 3, 3, 5, 5, 2, 2, 3, 3],  # "~~wwwwAA~~ww"
        [2, 3, 3, 3, 3, 5, 2, 2, 2, 2, 2, 3],  # "~wwwwA~~~~~w"
        [2, 2, 2, 2, 2, 2, 2, 4, 4, 4, 4, 4],  # "~~~~~~~====="
        [5, 5, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],  # "AA~~~~~~~~~~"
        [5, 5, 2, 2, 2, 2, 5, 2, 2, 4, 2, 2],  # "AA~~~~A~~=~~"
        [5, 2, 2, 2, 2, 2, 5, 5, 4, 4, 2, 2],  # "A~~~~~AA==~~"
        [2, 2, 3, 3, 3, 3, 5, 5, 2, 2, 3, 3],  # "~~wwwwAA~~ww"
        [2, 3, 3, 3, 3, 5, 2, 1, 2, 2, 2, 3],  # "~wwwwA~G~~~w"
        [2, 2, 2, 2, 2, 2, 2, 4, 4, 4, 4, 4],  # "~~~~~~~====="            
    ])
    MAX_STEPS = 100
```

```dqn_numa84.py
 99981/100000: episode: 1178, duration: 0.138s, episode steps: 27, steps per second: 196, episode reward: -26.000, mean reward: -0.963 [-1.000, 0.000], mean action: 1.407 [0.000, 3.000], mean observation: 2.991 [0.000, 6.000], loss: 4.588765, mean_absolute_error: 13.528292, mean_q: 19.509365
done, took 509.504 seconds
Testing for 5 episodes ...
Episode 1: reward: 65.000, steps: 15
Episode 2: reward: 35.000, steps: 15
Episode 3: reward: 55.000, steps: 15
Episode 4: reward: 45.000, steps: 15
Episode 5: reward: 45.000, steps: 15
```

```dqn_numa_duel_avg84.py
 99992/100000: episode: 1836, duration: 0.122s, episode steps: 21, steps per second: 172, episode reward: 40.000, mean reward: 1.905 [-1.000, 60.000], mean action: 1.571 [0.000, 3.000], mean observation: 2.993 [0.000, 6.000], loss: 5.780078, mean_absolute_error: 8.961402, mean_q: 13.373518
done, took 564.089 seconds
Testing for 5 episodes ...
Episode 1: reward: 46.000, steps: 15
Episode 2: reward: 56.000, steps: 15
Episode 3: reward: 46.000, steps: 15
Episode 4: reward: 46.000, steps: 15
Episode 5: reward: 66.000, steps: 15
```

```dqn_numa_duel_naive84.py
 99980/100000: episode: 1472, duration: 0.120s, episode steps: 19, steps per second: 158, episode reward: 31.000, mean reward: 1.632 [-1.000, 49.000], mean action: 1.421 [0.000, 3.000], mean observation: 2.992 [0.000, 6.000], loss: 5.345255, mean_absolute_error: 10.512672, mean_q: 15.310474
done, took 560.690 seconds
Testing for 5 episodes ...
Episode 1: reward: 55.000, steps: 15
Episode 2: reward: 65.000, steps: 15
Episode 3: reward: 65.000, steps: 15
Episode 4: reward: 55.000, steps: 15
Episode 5: reward: 55.000, steps: 15
```

```py
class MyEnv(gym.Env):
    metadata = {'render.modes': ['human', 'ansi']}
    FIELD_TYPES = [
        'S',  # 0: スタート
        'G',  # 1: ゴール
        '~',  # 2: 芝生(敵の現れる確率1/10)
        'w',  # 3: 森(敵の現れる確率1/2→3/4)
        '=',  # 4: 毒沼(1step毎に1のダメージ, 敵の現れる確率1/2)
        'A',  # 5: 山(歩けない)
        'Y',  # 6: 勇者
    ]
    MAP = np.array([
        [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],  # "AAAAAAAAAAAA"
        [5, 5, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],  # "AA~~~~~~~~~~"
        [5, 5, 2, 0, 2, 2, 5, 2, 2, 4, 2, 2],  # "AA~S~~A~~=~~"
        [5, 2, 2, 2, 2, 2, 5, 5, 4, 4, 2, 2],  # "A~~~~~AA==~~"
        [2, 2, 3, 3, 3, 3, 5, 5, 2, 2, 3, 3],  # "~~wwwwAA~~ww"
        [2, 3, 3, 3, 3, 5, 2, 2, 2, 2, 2, 3],  # "~wwwwA~~~~~w"
        [2, 2, 2, 2, 2, 2, 2, 4, 4, 4, 4, 4],  # "~~~~~~~====="
        [5, 5, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],  # "AA~~~~~~~~~~"
        [5, 5, 2, 2, 2, 2, 5, 2, 2, 4, 2, 2],  # "AA~~~~A~~=~~"
        [5, 2, 2, 2, 2, 2, 5, 5, 4, 4, 2, 2],  # "A~~~~~AA==~~"
        [2, 2, 3, 3, 3, 3, 5, 5, 2, 2, 3, 3],  # "~~wwwwAA~~ww"
        [2, 3, 3, 3, 3, 5, 2, 2, 2, 2, 2, 3],  # "~wwwwA~~~~~w"
        [2, 2, 2, 2, 2, 2, 2, 4, 4, 4, 4, 4],  # "~~~~~~~====="
        [5, 5, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],  # "AA~~~~~~~~~~"
        [5, 5, 2, 2, 2, 2, 5, 2, 2, 4, 2, 2],  # "AA~~~~A~~=~~"
        [5, 2, 2, 2, 2, 2, 5, 5, 4, 4, 2, 2],  # "A~~~~~AA==~~"
        [2, 2, 3, 3, 3, 3, 5, 5, 2, 2, 3, 3],  # "~~wwwwAA~~ww"
        [2, 3, 3, 3, 3, 5, 2, 2, 2, 2, 2, 3],  # "~wwwwA~~~~~w"
        [2, 2, 2, 2, 2, 2, 2, 4, 4, 4, 4, 4],  # "~~~~~~~====="
        [5, 5, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],  # "AA~~~~~~~~~~"
        [5, 5, 2, 2, 2, 2, 5, 2, 2, 4, 2, 2],  # "AA~~~~A~~=~~"
        [5, 2, 2, 2, 2, 2, 5, 5, 4, 4, 2, 2],  # "A~~~~~AA==~~"
        [2, 2, 3, 3, 3, 3, 5, 5, 2, 2, 3, 3],  # "~~wwwwAA~~ww"
        [2, 3, 3, 3, 3, 5, 2, 1, 2, 2, 2, 3],  # "~wwwwA~G~~~w"
        [2, 2, 2, 2, 2, 2, 2, 4, 4, 4, 4, 4],  # "~~~~~~~====="            
    ])
    MAX_STEPS = 100
```

```dqn_numa150.py
 499951/500000: episode: 2433, duration: 0.326s, episode steps: 65, steps per second: 199, episode reward: -64.000, mean reward: -0.985 [-1.000, 0.000], mean action: 1.400 [0.000, 3.000], mean observation: 2.921 [0.000, 6.000], loss: 0.202263, mean_absolute_error: 11.341081, mean_q: -13.744875
done, took 2471.709 seconds
Testing for 5 episodes ...
Episode 1: reward: -27.000, steps: 29
Episode 2: reward: -27.000, steps: 29
Episode 3: reward: -27.000, steps: 29
Episode 4: reward: 23.000, steps: 29
Episode 5: reward: -27.000, steps: 29
```

```dqn_numa_duel_avg150.py
 499986/500000: episode: 4473, duration: 0.232s, episode steps: 41, steps per second: 176, episode reward: -15.000, mean reward: -0.366 [-1.000, 25.000], mean action: 1.341 [0.000, 3.000], mean observation: 2.922 [0.000, 6.000], loss: 1.251979, mean_absolute_error: 12.011106, mean_q: -13.914641
done, took 2792.318 seconds
Testing for 5 episodes ...
Episode 1: reward: 0.000, steps: 27
Episode 2: reward: -20.000, steps: 27
Episode 3: reward: 0.000, steps: 27
Episode 4: reward: 10.000, steps: 27
Episode 5: reward: -10.000, steps: 27
```
dqn_numa_duel_naive.pyを学習すると学習は不十分だったが、そのパラメータを利用してdqn_numa_duel_avg150.pyで追加学習すると収束した。

```dqn_numa_duel_avg150.pyafterdqn_numa_duel_naive.py
 4997/5000: episode: 38, duration: 0.578s, episode steps: 104, steps per second: 180, episode reward: -103.000, mean reward: -0.990 [-1.000, 0.000], mean action: 1.433 [0.000, 3.000], mean observation: 2.922 [0.000, 6.000], loss: 0.721464, mean_absolute_error: 15.150576, mean_q: -18.862753
done, took 28.562 seconds
Testing for 5 episodes ...
Episode 1: reward: 4.000, steps: 27
Episode 2: reward: -26.000, steps: 27
Episode 3: reward: -6.000, steps: 27
Episode 4: reward: 4.000, steps: 27
Episode 5: reward: -6.000, steps: 27
```
###まとめ
・学習してみると、dqnとduel_avgの成績がよかった

・理論は不明である
・何を学習したんだろ？？
###おまけ

```dqn_numa150.py
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Episode 2: reward: 13.000, steps: 29
```

```dqn_numa_duel_avg150.pyafterdqn_numa_duel_naive.py
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Episode 5: reward: 24.000, steps: 27
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