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Dictum:  Is the true wisdom fortitude ambition. -- Napoleon 马尔可夫决策过程(Markov Decision Processes, MDPs)是一种对序列决策问题的解决工具,在这种问题中,决策者以序列方式与环境交互. "智能体-环境"交互的过程 首先,将MDPs引入强化学习.我们可以将智能体和环境的交互过程看成关于离散情况下时间步长\(t(t=0,1,2,3,\ldots)\)的序列:\(S_0,A_0,R_1,S_1,A_1…
为了实现某篇论文中的算法,得先学习下马尔可夫决策过程~ 1. https://leonardoaraujosantos.gitbooks.io/artificial-inteligence/content/markov_decision_process.html 2. https://www.cs.rice.edu/~vardi/dag01/givan1.pdf 3. http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/MDP.p…
In this post, I will illustrate Markov Property, Markov Reward Process and finally Markov Decision Process, which are fundamental concepts in Reinforcement Learning. Markov Property 'The state is independent of the past given the present' Markov Proc…
From the last post about MDP, we know the environment consists of 5 basic elements: S:State Space of environment; A:Actions Space that the environment allows; {Ps,s'}:Transition Matrix, the probabilities of how environment state transit from one to a…
一.前言 在第一章强化学习简介中,我们提到强化学习过程可以看做一系列的state.reward.action的组合.本章我们将要介绍马尔科夫决策过程(Markov Decision Processes)用于后续的强化学习研究中. 二.马尔科夫过程(Markov Processes) 2.1 马尔科夫性 首先,我们需要了解什么是马尔科夫性: 当我们处于状态StSt时,下一时刻的状态St+1St+1可以由当前状态决定,而不需要考虑历史状态. 未来独立于过去,仅仅于现在有关 将从状态s 转移到状态 s…
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Reinforcement Learning 对于控制决策问题的解决思路:设计一个回报函数(reward function),如果learning agent(如上面的四足机器人.象棋AI程序)在决定一步后,获得了较好的结果,那么我们给agent一些回报(比如回报函数结果为正),得到较差的结果,那么回报函数为负.比如,四足机器人,如果他向前走了一步(接近目标),那么回报函数为正,后退为负.如果我们能够对每一步进行评价,得到相应的回报函数,那么就好办了,我们只需要找到一条回报值最大的路径(每步的回…
本文转自:http://www.pomdp.org/ 一.Background on POMDPs We assume that the reader is familiar with the value iteration algorithm for regular discrete Markov decision processes (MDPs). However, we will need to differentiate these from POMDPs which we could…
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