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A Modified Memory-Based Reinforcement Learning Method for Solving POMDP Problems

Content Provider	Springer Nature Link
Author	Zheng, Lei Cho, Siu Yeung
Copyright Year	2011
Abstract	Partially observable Markov decision processes (POMDP) provide a mathematical framework for agent planning under stochastic and partially observable environments. The classic Bayesian optimal solution can be obtained by transforming the problem into Markov decision process (MDP) using belief states. However, because the belief state space is continuous and multi-dimensional, the problem is highly intractable. Many practical heuristic based methods are proposed, but most of them require a complete POMDP model of the environment, which is not always practical. This article introduces a modified memory-based reinforcement learning algorithm called modified U-Tree that is capable of learning from raw sensor experiences with minimum prior knowledge. This article describes an enhancement of the original U-Tree’s state generation process to make the generated model more compact, and also proposes a modification of the statistical test for reward estimation, which allows the algorithm to be benchmarked against some traditional model-based algorithms with a set of well known POMDP problems.
Starting Page	187
Ending Page	200
Page Count	14
File Format	PDF
ISSN	13704621
Journal	Neural Processing Letters
Volume Number	33
Issue Number	2
e-ISSN	1573773X
Language	English
Publisher	Springer US
Publisher Date	2011-02-19
Publisher Place	Boston
Access Restriction	Subscribed
Subject Keyword	Memory-based reinforcement learning Markov decision processes Partially observable Markov decision processes Reinforcement learning Computational Intelligence Artificial Intelligence (incl. Robotics) Statistical Physics, Dynamical Systems and Complexity
Content Type	Text
Resource Type	Article
Subject	Neuroscience Artificial Intelligence Computer Networks and Communications Software

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