This object implements a Q-value function approximator that you can use as a critic for a reinforcement learning agent. A Q-value function (also known as action-value function) is a mapping from an environment
答案:这题跟上题的区别就是我们使用Q而不是V,使用Q function的话,就可以很简单的take max over action,只需要对不同的actions将网络向前推导。这也是为什么我们使用Q-learning而不是V-learning 当我们不知道transition model的时候。 问题3:上述Q-learning的方法能否保证获得一个对state action value function最优的...
q value function贝尔曼方程Q值函数(Q Function)是动作价值函数的简称,它是一种评估在给定状态下采取某个动作的价值大小的函数。在强化学习中,Q值函数被用来估计在某个状态下采取某个动作的预期回报。 贝尔曼方程(Bellman Equation)是强化学习中用于描述Q值函数的一个重要公式。它表示当前状态的价值和下一时刻状态的...
写这篇文章的初衷是因为很多网上关于soft Q-learning的文章都是直接给的soft Q-value function的定义,没有去讲为什么这么定义,这篇文章主要是讲一下为什么会这么定义,所谓的soft到底是什么思想 一、波尔兹曼分布 1.引入 若已知一个关于(s,a)的函数ε(s,a),我们想要一个分布q∗(a|s), 同时令ε(s,A)...
Schulte, O., Zhao, Z. & Routley, K. (2015), `What is the value of an Action in Ice Hockey? Learning a Q-function for the NHL.'.Schulte, O., Z. Zhao, and K. Routley. 2015. "What is the Value of an Action in Ice Hockey? Learning a Q-function for the NHL." in ...
When we run theqvaluefunction with anfdr.level = 0.01argument, we get: qobj_fdrlevel=qvalue(p=hedenfalk$p,fdr.level=0.05) head(qobj_fdrlevel$significant);length(qobj_fdrlevel$significant) ## [1]FALSEFALSEFALSEFALSEFALSEFALSE ## [1] 3170 ...
We propose a new simple and natural algorithm for learning the optimal $Q$-value function of a discounted-cost Markov Decision Process (MDP) when the transition kernels are unknown. Unlike the classical learning algorithms for MDPs, such as $Q$-learning and `actor-critic' algorithms, this ...
Additionally, to prevent overly conservative estimates, we introduce an uncertainty-aware optimization objective for updating the Q-value function. The proposed QDQ demonstrates solid theoretical guarantees for the accuracy of Q-value distribution learning and uncertainty measurement, as well as the ...
For a fixed target Q-value QTarget, we define the notion of “Q-function minus Q target”, Qk=Qk(s,a)−QTarget. We reformulate the Qk+1 expression by Qk as: (9)Qk+1=r−(1−γ)QTarget+γQk−h⋅Es,a∼Dμ(a∣s)πˆβ(a∣s)∑i=0kQi. This refined expression hig...
For the i-th test with respective test statistic ti , this can be written as: q -value(ti ) = pFDR(T ti ) (10) It is possible to show that the q -value as a function of the test statistic ti can additionally be written: q -value(ti ) = Pr(Hi = 0 T ti ) (11) This ...