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43 lines
1.5 KiB
Python
43 lines
1.5 KiB
Python
import gym
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import sys
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from dqn_agent import *
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class BasicTask:
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def transfer_state(self, state):
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return state
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def reset(self):
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return self.transfer_state(self.env.reset())
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def step(self, action):
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next_state, reward, done, info = self.env.step(action)
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next_state = self.transfer_state(next_state)
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return next_state, reward, done, info
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class MountainCar(BasicTask):
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state_space_size = 2
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action_space_size = 3
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name = 'MountainCar-v0'
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def __init__(self):
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self.env = gym.make(self.name)
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self.env._max_episode_steps = sys.maxsize
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if __name__ == '__main__':
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task = MountainCar()
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optimizer_fn = lambda name: tf.train.GradientDescentOptimizer(name=name, learning_rate=0.01)
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network_fn = lambda name: Network(name, task.state_space_size,
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task.action_space_size, optimizer_fn, tf.random_normal_initializer())
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policy_fn = lambda: GreedyPolicy(epsilon=0.5, decay_factor=0.95, min_epsilon=0.1)
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replay_fn = lambda: Replay(memory_size=10000, batch_size=10)
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agent = DQNAgent('mountain-car', task, network_fn, policy_fn, replay_fn,
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discount=0.99, step_limit=5000, target_network_update_freq=1000)
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with tf.Session() as sess:
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sess.run(tf.global_variables_initializer())
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ep = 0
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while True:
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ep += 1
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reward = agent.episode(sess)
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print 'episode %d: %f' % (ep, reward)
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if reward > -110:
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break
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