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…

网络安全问题的背景 网络安全研究的内容包括很多方面,作者形象比喻为盲人摸象,不同领域的网络安全专家对网络安全的认识是不同的. For researchers in the field of cryptography, security is all about cryptographic algorithms and hash functions. Those who are in information security focus mainly on privacy, watermarkin…

Multi-shot Pedestrian Re-identification via Sequential Decision Making 2019-07-31 20:33:37 Paper: http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhang_Multi-Shot_Pedestrian_Re-Identification_CVPR_2018_paper.pdf Code: https://github.com/TuSimpl…

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…

The Baum-Welch algorithm is commonly used for training a Hidden Markov Model because of its superior numerical stability and its ability to guarantee the discovery of a locally maximum, Maximum Likelihood Estimator, in the presence of incomplete trai…

Problems[show] Classification Clustering Regression Anomaly detection Association rules Reinforcement learning Structured prediction Feature engineering Feature learning Online learning Semi-supervised learning Unsupervised learning Learning to rank…