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@@ -5,14 +5,14 @@ import itertools
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import gym
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import numpy
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-_OBSERVATION_STEPS_NUM = 64
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+_OBSERVATION_STEPS_NUM = 24
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_LEARNING_RATE = 0.1
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-_DISCOUNT = 0.9
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+_DISCOUNT = 0.95
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def _main():
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env = gym.make("CartPole-v0")
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- observation_min = numpy.array([-4.8, -4.0, -0.8, -3.6])
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+ observation_min = numpy.array([-4.8, -4.8, -0.9, -4.0])
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assert (
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env.observation_space.shape == observation_min.shape
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), env.observation_space.shape
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@@ -20,8 +20,8 @@ def _main():
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observation_step = (observation_max - observation_min) / _OBSERVATION_STEPS_NUM
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assert len(env.observation_space.shape) == 1
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q_table = numpy.random.uniform(
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- low=0,
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- high=4,
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+ low=-4,
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+ high=0,
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size=(
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[_OBSERVATION_STEPS_NUM] * (env.observation_space.shape[0])
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+ [env.action_space.n]
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@@ -30,7 +30,7 @@ def _main():
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print("q_table:", q_table.itemsize * q_table.size / 2 ** 20, "MiB")
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for episode_index in itertools.count():
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observation = env.reset()
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- render = (episode_index % 500) == 0
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+ render = (episode_index % 400) == 0
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if render:
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env.render()
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observation_index = ((observation - observation_min) / observation_step).astype(
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@@ -48,7 +48,9 @@ def _main():
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(next_observation - observation_min) / observation_step
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).astype(int)
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if done:
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- q_table[tuple(observation_index)][action] = reward
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+ q_table[tuple(observation_index)][action] = (
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+ -300 if step_index < 190 else reward
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+ )
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print(step_index + 1, "steps")
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break
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assert not info, info
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