update RL_example.py

docs/chapter1/RL_example.py

@@ -2,7 +2,7 @@ import gym
 import numpy as np
 
 
-class BespokeAgent:
+class Agent:
     def __init__(self, env):
         pass
     
@@ -21,7 +21,7 @@ class BespokeAgent:
         pass
     
 
-def play_montecarlo(env, agent, render=False, train=False):
+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
@@ -39,16 +39,16 @@ def play_montecarlo(env, agent, render=False, train=False):
 
 
 env = gym.make('MountainCar-v0')
-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
+env.seed(3) # 设置随机种子，让结果可复现
-agent = BespokeAgent(env)
+agent = Agent(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_montecarlo(env, agent, render=True)
+episode_reward = play(env, agent, render=True)
 print('回合奖励 = {}'.format(episode_reward))
 
-episode_rewards = [play_montecarlo(env, agent) for _ in range(100)]
+episode_rewards = [play(env, agent) for _ in range(100)]
 print('平均回合奖励 = {}'.format(np.mean(episode_rewards)))