59 lines
1.6 KiB
Python
59 lines
1.6 KiB
Python
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from RL_brain import DeepQNetwork
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import os
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import sys
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from env import PartitionMazeEnv
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def run_maze():
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step = 0 # 为了记录走到第几步，记忆录中积累经验（也就是积累一些transition）之后再开始学习
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for episode in range(200):
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# initial observation
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observation = env.reset()
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while True:
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# refresh env
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env.render()
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# RL choose action based on observation
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action = RL.choose_action(observation)
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# RL take action and get next observation and reward
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observation_, reward, done = env.step(action)
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# !! restore transition
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RL.store_transition(observation, action, reward, observation_)
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# 超过200条transition之后每隔5步学习一次
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if (step > 200) and (step % 5 == 0):
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RL.learn()
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# swap observation
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observation = observation_
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# break while loop when end of this episode
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if done:
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break
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step += 1
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# end of game
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print("game over")
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env.destroy()
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if __name__ == "__main__":
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# maze game
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env = PartitionMazeEnv()
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# TODO 代码还没有写完，跑不了！！！
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RL = DeepQNetwork(env.n_actions, env.n_features,
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learning_rate=0.01,
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reward_decay=0.9,
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e_greedy=0.9,
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replace_target_iter=200,
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memory_size=2000)
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env.after(100, run_maze)
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env.mainloop()
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RL.plot_cost()
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