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作者: “蜗牛超速发布于: 2024-04-10 执行: 131 克隆: 0

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点击【Run】按钮将运行下方代码。可编辑、修改代码尝试新的运行逻辑。

为自己编写一个 MiniPy 吧，或者一键克隆这个吧！

from maze_env import Maze
from RL_brain import DeepQNetwork

def run_maze():
    step = 0
    for episode in range(300):
        # initial observation
        observation = env.reset()

while True:
            # fresh env
            env.render()

# RL choose action based on observation
            action = RL.choose_action(observation)

# RL take action and get next observation and reward
            observation_, reward, done = env.step(action)

RL.store_transition(observation, action, reward, observation_)

if (step > 200) and (step % 5 == 0):
                RL.learn()

# swap observation
            observation = observation_

# break while loop when end of this episode
            if done:
                break
            step += 1

# end of game
    print('game over')
    env.destroy()

if __name__ == "__main__":
    # maze game
    env = Maze()
    RL = DeepQNetwork(env.n_actions, env.n_features,
                      learning_rate=0.01,
                      reward_decay=0.9,
                      e_greedy=0.9,
                      replace_target_iter=200,
                      memory_size=2000,
                      # output_graph=True
                      )
    env.after(100, run_maze)
    env.mainloop()
    RL.plot_cost()