55 lines
2.0 KiB
Python
Executable File
55 lines
2.0 KiB
Python
Executable File
import gym
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import numpy as np
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class BespokeAgent:
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def __init__(self, env):
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pass
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def decide(self, observation): # 决策
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position, velocity = observation
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lb = min(-0.09 * (position + 0.25) ** 2 + 0.03,
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0.3 * (position + 0.9) ** 4 - 0.008)
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ub = -0.07 * (position + 0.38) ** 2 + 0.07
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if lb < velocity < ub:
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action = 2
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else:
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action = 0
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return action # 返回动作
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def learn(self, *args): # 学习
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pass
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def play_montecarlo(env, agent, render=False, train=False):
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episode_reward = 0. # 记录回合总奖励，初始化为0
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observation = env.reset() # 重置游戏环境，开始新回合
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while True: # 不断循环，直到回合结束
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if render: # 判断是否显示
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env.render() # 显示图形界面，图形界面可以用 env.close() 语句关闭
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action = agent.decide(observation)
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next_observation, reward, done, _ = env.step(action) # 执行动作
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episode_reward += reward # 收集回合奖励
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if train: # 判断是否训练智能体
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agent.learn(observation, action, reward, done) # 学习
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if done: # 回合结束，跳出循环
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break
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observation = next_observation
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return episode_reward # 返回回合总奖励
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env = gym.make('MountainCar-v0')
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env.seed(3) # 设置随机数种子,只是为了让结果可以精确复现,一般情况下可删去
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agent = BespokeAgent(env)
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print('观测空间 = {}'.format(env.observation_space))
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print('动作空间 = {}'.format(env.action_space))
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print('观测范围 = {} ~ {}'.format(env.observation_space.low,
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env.observation_space.high))
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print('动作数 = {}'.format(env.action_space.n))
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episode_reward = play_montecarlo(env, agent, render=True)
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print('回合奖励 = {}'.format(episode_reward))
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episode_rewards = [play_montecarlo(env, agent) for _ in range(100)]
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print('平均回合奖励 = {}'.format(np.mean(episode_rewards)))
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