test_limited_action_gridworld.py 文件源码

def _build_policy(env, predictor, epsilon):
    eye = np.eye(env.num_states)
    q_values = predictor.predict(
        {str(i): eye[i]
         for i in range(env.num_states)}
    )
    policy_vector = [
        env.ACTIONS[np.argmax([q_values[action][i] for action in env.ACTIONS])]
        for i in range(env.num_states)
    ]

    def policy(state) -> str:
        if np.random.random() < epsilon:
            return np.random.choice(env.ACTIONS)
        else:
            return policy_vector[state]

    return policy