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...
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...
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 ...
Given a set of p-values, the qvalue object can be calculated by using theqvaluefunction: library(qvalue) data(hedenfalk)pvalues<-hedenfalk$pqobj<-qvalue(p=pvalues) Additionally, the qvalue object can be calculated given a set of empirical null statistics: ...
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 ...
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement 湫汐湫兮 4336 0 [论文讲解]QTRAN:Learning to Factorize with Transformation for Cooperative Multi-A... 湫汐湫兮 673 2 [论文讲解]ResQ:A Residual Q Function-based Approach for Multi-Agent Reinforcement... 湫汐湫兮 ...
The Q-value cumulates the reward received during a learning trial and is used as the fitness function for PSO evolution. During the trail, one particle is selected from the swarm; meanwhile, a corresponding NFS is built and applied to the environment with an immediate feedback reward. The ...