目前在用python做强化学习,刚入门,有个问题不是很明白。
QLearning的代码是:
import numpy as np
import math
class QLearning(object):
def __init__(self, state_dim: object, action_dim: object, cfg: object) -> object:
self.action_dim = action_dim # dimension of acgtion
self.lr = cfg.lr # learning rate
self.gamma = cfg.gamma
self.epsilon = 0
self.sample_count = 0
self.epsilon_start = cfg.epsilon_start
self.epsilon_end = cfg.epsilon_end
self.epsilon_decay = cfg.epsilon_decay
self.Q_table = np.zeros((state_dim, action_dim)) # Q表
def choose_action(self, state):
self.sample_count += 1
self.epsilon = self.epsilon_end + (self.epsilon_start - self.epsilon_end) * \
math.exp(-1. * self.sample_count / self.epsilon_decay)
if np.random.uniform(0, 1) > self.epsilon: # 随机选取0-1之间的值,如果大于epsilon就按照贪心策略选取action,否则随机选取
action = self.predict(state)
else:
action = np.random.choice(self.action_dim) #有一定概率随机探索选取一个动作
return action
def predict(self, state):
'''根据输入观测值,采样输出的动作值,带探索,测试模型时使用
'''
Q_list = self.Q_table[state, :]
Q_max = np.max(Q_list)
action_list = np.where(Q_list == Q_max)[0]
action = np.random.choice(action_list) # Q_max可能对应多个 action ,可以随机抽取一个
return action
def update(self, state, action, reward, next_state, done):
Q_predict = self.Q_table[state, action]
if done:
Q_target = reward # 没有下一个状态了
else:
Q_target = reward + self.gamma * np.max(
self.Q_table[next_state, :]) # Q_table-learning
self.Q_table[state, action] += self.lr * (Q_target - Q_predict) # 修正q
def save(self,path):
np.save(path+"Q_table.npy", self.Q_table)
def load(self, path):
self.Q_table = np.load(path+"Q_table.npy")
做测试的代码是:
import gym
from QLearning.agent1 import QLearning
from envs.gridworld_env import CliffWalkingWapper
env = gym.make("CliffWalking-v0") # 0 up, 1 right, 2 down, 3 left
env = CliffWalkingWapper(env)
agent = QLearning(
obs_dim=env.observation_space.n,
action_dim=env.action_space.n,
learning_rate = cfg.lr,
gamma = cfg.gamma,
epsilon_start = cfg.epsilon_start, epsilon_end = cfg.epsilon_end, epsilon_decay=cfg.epsilon_decay)
render = False # 是否打开GUI画面
rewards = [] # 记录所有episode的reward
MA_rewards = [] # 记录滑动平均的reward
steps = [] # 记录所有episode的steps
for i_episode in range(1, cfg.max_episodes + 1):
ep_reward = 0 # 记录每个episode的reward
ep_steps = 0 # 记录每个episode走了多少step
obs = env.reset() # 重置环境, 重新开一局(即开始新的一个episode)
while True:
action = agent.sample(obs) # 根据算法选择一个动作
next_obs, reward, done, _ = env.step(action) # 与环境进行一个交互
# 训练 Q-learning算法
agent.learn(obs, action, reward, next_obs, done) # 不需要下一步的action
obs = next_obs # 存储上一个观察值
ep_reward += reward
ep_steps += 1 # 计算step数
if render:
env.render() # 渲染新的一帧图形
if done:
break
steps.append(ep_steps)
rewards.append(ep_reward)
# 计算滑动平均的reward
if i_episode == 1:
MA_rewards.append(ep_reward)
else:
MA_rewards.append(
0.9 * MA_rewards[-1] + 0.1 * ep_reward)
print('Episode %s: steps = %s , reward = %.1f, explore = %.2f' % (i_episode, ep_steps,
ep_reward, agent.epsilon))
agent.save() # 训练结束,保存模型
我用的是pycharm, 为什么cfg.是报错呢(下划线为红色)? 我始终想不明白