Q-learning:直接用最优的动作即:q(s,a)=E[R_{t+1}+\gamma\max_aq(S_{t+1},a)|S_t=s,A_t=a]. 7.值函数逼近: 传统方法:插值方法或者从核方法的角度去逼近。 目前用神经网络去逼近函数。 state value的目标函数最小二乘逼近: \begin{align} J(w)&=E[(v_\pi(S)-\tilde{v}(S,w))^...
如上图,Bellman 方程也可以表达成矩阵形式:v=R+\gamma Pv,可直接求出v=(I-\gamma P)^{-1}R;其复杂度为O(n^3),一般可通过动态规划、蒙特卡洛估计与 Temporal-Difference learning 求解。 状态价值函数和动作价值函数的关系 v_{\pi}(s) = \sum_{a \in A} \pi(a|s)q_{\pi}(s,a) = E[q_{...
Q-learning算法的核心就是我们1.3中介绍的Bellman optimality equation,即: Q-learning是RL的很经典的算法,但有个很大的问题在于它是一种表格方法,也就是说它非常的直来之前,就是根据过去出现过的状态,统计和迭代Q值。一方面Q-learning适用的状态和动作空间非常小;另一方面但如果一个状态从未出现过,Q-learning是无法...
building upon the Bellman equation to update Q-values iteratively. The Q-learning update equation encapsulates this iterative process, where Q-values for state-action pairs are refined based on observed experiences. This iterative learning
Q-learning is at the heart of all reinforcement learning. AlphaGO winning against Lee Sedol or DeepMind crushing old Atari games are both fundamentally Q-learning with sugar on top. At the heart of Q-learning are things like the Markov decision process (MDP) and the Bellman equation . While...
action}pair. When in a particular state, the agent will take the action which corresponds to the maximum value. Initialising the q-table depends upon the heuristics the same as in the case of neural-network weights. We can update the values of the q-table(q-values) by the equation given...
在Q-Learning算法中,用Q(s,a)表示最大的折扣后序激励 Q(St,at) = max R(t+1),物理意义可以认为是在状态s下采取动作a在游戏结束时我们可能拿到的最好的成绩,也就是所谓的Q函数 类似折扣激励的表示,Q函数也可以表示为: Q(s,a) = r(t) + γ*maxQ(S`,a`),高大上的名字叫Bellman equation贝尔曼方...
Q-learning Sarsa MDPs 还有下面几种扩展形式: Infinite and continuous MDPs Partially observable MDPs Undiscounted, average reward MDPs 动态规划求解 MDPs 的 Planning 动态规划是一种通过把复杂问题划分为子问题,并对自问题进行求解,最后把子问题的解结合起来解决原问题的方法。「动态」是指问题由一系列的状态...
Before exploring,the Q-table gives the same arbitrary fixed value(most of time 0).As we explore the environment,the Q-table will give us a better and better approximation by iteratively updating Q(s,a) using Bellman Equation. Step1: Initialize Q-values ...
一、Bellman Equation for Return 一般而言,从任何状态的return可分为两个部分:①the immediate reward from the action to reach the next state(到达下一state的即时奖励);②the Discounted Return from that next state by following the same policy for all subsequent steps(所有后续步骤遵循相同的policy从下一...