从SARSA算法到Q-learning with ϵ-greedy Exploration算法

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本文链接:https://blog.csdn.net/Solo95/article/details/102762027

这篇博文是Model-Free Control的一部分,事实上SARSA和Q-learning with ϵ-greedy Exploration都是不依赖模型的控制的一部分,如果你想要全面的了解它们,建议阅读原文。

SARSA Algorithm

SARSA代表state,action,reward,next state,action taken in next state,算法在每次采样到该五元组时更新,所以得名SARSA。

1: Set1:\ Set1: Set Initial ϵ\epsilonϵ-greedy policy π,t=0\pi,t=0π,t=0, initial state st=s0s_t=s_0st​=s0​ 2: Take at∼π(st)2:\ Take \ a_t \sim \pi(s_t)2: Take at​∼π(st​) // Sample action from policy 3: Observe (rt,st+1)3:\ Observe \ (r_t, s_{t+1})3: Observe (rt​,st+1​) 4: loop4:\ loop4: loop 5: Take5:\ \quad Take5: Take action at+1∼π(st+1)a_{t+1}\sim \pi(s_{t+1})at+1​∼π(st+1​) 6: Observe (rt+1,st+2)6:\ \quad Observe \ (r_{t+1},s_{t+2})6: Observe (rt+1​,st+2​) 7: Q(st,at)←Q(st,at)+α(rt+γQ(st+1,at+1)−Q(st,at))7:\ \quad Q(s_t,a_t) \leftarrow Q(s_t,a_t)+\alpha(r_t+\gamma Q(s_{t+1},a_{t+1})-Q(s_t,a_t))7: Q(st​,at​)←Q(st​,at​)+α(rt​+γQ(st+1​,at+1​)−Q(st​,at​)) 8: π(st)=argmax Q(st,a)w.prob 1−ϵ,else random8:\ \quad \pi(s_t) = \mathop{argmax} \ Q(s_t,a) w.prob\ 1-\epsilon, else \ random8: π(st​)=argmax Q(st​,a)w.prob 1−ϵ,else random 9: t=t+19:\ t=t+19: t=t+1 10:end loop10: end \ loop10:end loop

Q-learing: Learning the Optimal State-Action Value

我们能在不知道π∗\pi^*π∗的情况下估计最佳策略π∗\pi^*π∗的价值吗?

可以。使用Q-learning。

核心思想: 维护state-action Q值的估计并且使用它来bootstrap最佳未来动作的的价值。

回顾SARSA Q(st,at)←Q(st,at)+α((rt+γQ(st+1,at+1))−Q(st,at))Q(s_t,a_t)\leftarrow Q(s_t,a_t)+\alpha((r_t+\gamma Q(s_{t+1},a_{t+1}))-Q(s_t,a_t))Q(st​,at​)←Q(st​,at​)+α((rt​+γQ(st+1​,at+1​))−Q(st​,at​))

Q-learning Q(st,at)←Q(st,at)+α((rt+γmaxa′Q(st+1,a′)−Q(st,at)))Q(s_t,a_t)\leftarrow Q(s_t,a_t)+\alpha((r_t+\gamma \mathop{max}\limits_{a'}Q(s_{t+1},a')-Q(s_t,a_t)))Q(st​,at​)←Q(st​,at​)+α((rt​+γa′max​Q(st+1​,a′)−Q(st​,at​)))

Off-Policy Control Using Q-learning

  • 在上一节中假定了有某个策略πb\pi_bπb​可以用来执行
  • πb\pi_bπb​决定了实际获得的回报
  • 现在在来考虑如何提升行为策略(policy improvement)
  • 使行为策略πb\pi_bπb​是对(w.r.t)当前的最佳Q(s,a)Q(s,a)Q(s,a)估计的- ϵ\epsilonϵ-greedy策略

Q-learning with ϵ\epsilonϵ-greedy Exploration

1: Intialize Q(s,a),∀s∈S,a∈A t=0,1:\ Intialize \ Q(s,a), \forall s \in S, a \in A \ t=0,1: Intialize Q(s,a),∀s∈S,a∈A t=0, initial state st=s0s_t=s_0st​=s0​ 2: Set πb2:\ Set \ \pi_b2: Set πb​ to be ϵ\epsilonϵ-greedy w.r.t. Q$ 3: loop3:\ loop3: loop 4: Take at∼πb(st)4:\ \quad Take \ a_t \sim\pi_b(s_t)4: Take at​∼πb​(st​) // simple action from policy 5: Observe (rt,st+1)5:\ \quad Observe \ (r_t, s_{t+1})5: Observe (rt​,st+1​) 6: Update Q6:\ \quad Update \ Q6: Update Q given (st,at,rt,st+1)(s_t,a_t,r_t,s_{t+1})(st​,at​,rt​,st+1​) 7: Q(sr,ar)←Q(st,rt)+α(rt+γmaxaQ(st1,a)−Q(st,at))7:\ \quad Q(s_r,a_r) \leftarrow Q(s_t,r_t)+\alpha(r_t+\gamma \mathop{max}\limits_{a}Q(s_{t1},a)-Q(s_t,a_t))7: Q(sr​,ar​)←Q(st​,rt​)+α(rt​+γamax​Q(st1​,a)−Q(st​,at​)) 8: Perform8:\ \quad Perform8: Perform policy impovement: set πbset \ \pi_bset πb​ to be ϵ\epsilonϵ-greedy w.r.t Q 9: t=t+19:\ \quad t=t+19: t=t+1 10:end loop10: end \ loop10:end loop

如何初始化QQQ重要吗? 无论怎样初始化QQQ(设为0,随机初始化)都会收敛到正确值,但是在实际应用上非常重要,以最优化初始化形式初始化它非常有帮助。会在exploration细讲这一点。

例题

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