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