前言
本篇博文通过悬崖寻路这一实例来实现Sarsa和Q-Learning算法。
相关代码主要参考自PARL强化学习公开课。.
理论基础
这里简单放一下Sarsa和Q-Learning的更新公式，更详细的内容可参看本专栏后续的知识点整理。
Sarsa：
Q-Learning：
环境介绍
如图所示，红色区域表示悬崖。左下角是起点，右下角是终点。每走一步会获得-1的回报，跌落悬崖会获得-100的回报。智能体需要找到一条路径到达终点，不落入悬崖。
关于Sarsa和Q-Learning的区别，在本专栏的上一篇博文【强化学习】迷宫寻宝：Sarsa和Q-Learning中，已经做了详细的分析，这里不再赘述。
Sarsa
代码语言：javascript复制import gym
import numpy as np
import time
class SarsaAgent(object):
def __init__(self, obs_n, act_n, learning_rate=0.01, gamma=0.9, e_greed=0.1):
self.act_n = act_n # 动作维度，有几个动作可选
self.lr = learning_rate # 学习率
self.gamma = gamma # reward的衰减率
self.epsilon = e_greed # 按一定概率随机选动作
self.Q = np.zeros((obs_n, act_n))
# 根据输入观察值，采样输出的动作值，带探索
def sample(self, obs):
if np.random.uniform(0, 1) < (1.0 - self.epsilon): # 根据table的Q值选动作
action = self.predict(obs)
else:
action = np.random.choice(self.act_n) # 有一定概率随机探索选取一个动作
return action
# 根据输入观察值，预测输出的动作值
def predict(self, obs):
Q_list = self.Q[obs, :]
maxQ = np.max(Q_list)
action_list = np.where(Q_list == maxQ)[0] # maxQ可能对应多个action
action = np.random.choice(action_list)
return action
# 学习方法，也就是更新Q-table的方法
def learn(self, obs, action, reward, next_obs, next_action, done):
""" on-policy
obs: 交互前的obs, s_t
action: 本次交互选择的action, a_t
reward: 本次动作获得的奖励r
next_obs: 本次交互后的obs, s_t 1
next_action: 根据当前Q表格, 针对next_obs会选择的动作, a_t 1
done: episode是否结束
"""
predict_Q = self.Q[obs, action]
if done:
target_Q = reward # 没有下一个状态了
else:
target_Q = reward self.gamma * self.Q[next_obs, next_action] # Sarsa
self.Q[obs, action] = self.lr * (target_Q - predict_Q) # 修正q
# 保存Q表格数据到文件
def save(self):
npy_file = './q_table.npy'
np.save(npy_file, self.Q)
print(npy_file ' saved.')
# 从文件中读取Q值到Q表格中
def restore(self, npy_file='./q_table.npy'):
self.Q = np.load(npy_file)
print(npy_file ' loaded.')
def run_episode(env, agent, render=False):
total_steps = 0 # 记录每个episode走了多少step
total_reward = 0
obs = env.reset() # 重置环境, 重新开一局（即开始新的一个episode）
action = agent.sample(obs) # 根据算法选择一个动作
while True:
next_obs, reward, done, _ = env.step(action) # 与环境进行一个交互
next_action = agent.sample(next_obs) # 根据算法选择一个动作
# 训练 Sarsa 算法
agent.learn(obs, action, reward, next_obs, next_action, done)
action = next_action
obs = next_obs # 存储上一个观察值
total_reward = reward
total_steps = 1 # 计算step数
if render:
env.render() # 渲染新的一帧图形
if done:
break
return total_reward, total_steps
def test_episode(env, agent):
total_reward = 0
obs = env.reset()
while True:
action = agent.predict(obs) # greedy
next_obs, reward, done, _ = env.step(action)
total_reward = reward
obs = next_obs
time.sleep(0.5)
env.render()
if done:
break
return total_reward
if __name__ == '__main__':
# 使用gym创建悬崖环境
env = gym.make("CliffWalking-v0") # 0 up, 1 right, 2 down, 3 left
# 创建一个agent实例，输入超参数
agent = SarsaAgent(
obs_n=env.observation_space.n,
act_n=env.action_space.n,
learning_rate=0.1,
gamma=0.9,
e_greed=0.1)
# 训练500个episode，打印每个episode的分数
for episode in range(500):
ep_reward, ep_steps = run_episode(env, agent, False)
print('Episode %s: steps = %s , reward = %.1f' % (episode, ep_steps, ep_reward))
# 全部训练结束，查看算法效果
test_reward = test_episode(env, agent)
print('test reward = %.1f' % (test_reward))Q-Learning
代码语言：javascript复制import gym
import time
import numpy as np
class QLearningAgent(object):
def __init__(self, obs_n, act_n, learning_rate=0.01, gamma=0.9, e_greed=0.1):
self.act_n = act_n # 动作维度，有几个动作可选
self.lr = learning_rate # 学习率
self.gamma = gamma # reward的衰减率
self.epsilon = e_greed # 按一定概率随机选动作
self.Q = np.zeros((obs_n, act_n))
# 根据输入观察值，采样输出的动作值，带探索
def sample(self, obs):
if np.random.uniform(0, 1) < (1.0 - self.epsilon): # 根据table的Q值选动作
action = self.predict(obs)
else:
action = np.random.choice(self.act_n) # 有一定概率随机探索选取一个动作
return action
# 根据输入观察值，预测输出的动作值
def predict(self, obs):
Q_list = self.Q[obs, :]
maxQ = np.max(Q_list)
action_list = np.where(Q_list == maxQ)[0] # maxQ可能对应多个action
action = np.random.choice(action_list)
return action
# 学习方法，也就是更新Q-table的方法
def learn(self, obs, action, reward, next_obs, done):
""" off-policy
obs: 交互前的obs, s_t
action: 本次交互选择的action, a_t
reward: 本次动作获得的奖励r
next_obs: 本次交互后的obs, s_t 1
done: episode是否结束
"""
predict_Q = self.Q[obs, action]
if done:
target_Q = reward # 没有下一个状态了
else:
target_Q = reward self.gamma * np.max(self.Q[next_obs, :]) # Q-learning
self.Q[obs, action] = self.lr * (target_Q - predict_Q) # 修正q
# 把 Q表格 的数据保存到文件中
def save(self):
npy_file = './q_table.npy'
np.save(npy_file, self.Q)
print(npy_file ' saved.')
# 从文件中读取数据到 Q表格
def restore(self, npy_file='./q_table.npy'):
self.Q = np.load(npy_file)
print(npy_file ' loaded.')
def run_episode(env, agent, render=False):
total_steps = 0 # 记录每个episode走了多少step
total_reward = 0
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)
obs = next_obs # 存储上一个观察值
total_reward = reward
total_steps = 1 # 计算step数
if render:
env.render() # 渲染新的一帧图形
if done:
break
return total_reward, total_steps
def test_episode(env, agent):
total_reward = 0
obs = env.reset()
while True:
action = agent.predict(obs) # greedy
next_obs, reward, done, _ = env.step(action)
total_reward = reward
obs = next_obs
time.sleep(0.5)
env.render()
if done:
break
return total_reward
if __name__ == '__main__':
# 使用gym创建悬崖环境
env = gym.make("CliffWalking-v0") # 0 up, 1 right, 2 down, 3 left
# 创建一个agent实例，输入超参数
agent = QLearningAgent(
obs_n=env.observation_space.n,
act_n=env.action_space.n,
learning_rate=0.1,
gamma=0.9,
e_greed=0.1)
# 训练500个episode，打印每个episode的分数
for episode in range(500):
ep_reward, ep_steps = run_episode(env, agent, False)
print('Episode %s: steps = %s , reward = %.1f' % (episode, ep_steps, ep_reward))
# 全部训练结束，查看算法效果
test_reward = test_episode(env, agent)
print('test reward = %.1f' % (test_reward))运行结果
参考资料
PARL强化学习公开课
强化学习之Q-learning与Sarsa算法解决悬崖寻路问题
