Reinforcement Learning: Stochastic Approximation Algorithms for Markov Decision Processes

TL;DR

This paper discusses stochastic approximation algorithms in reinforcement learning for Markov decision processes, highlighting their potential use in partially observed scenarios and providing a concise overview of their principles.

Contribution

It offers a clear and concise description of stochastic approximation algorithms applicable to reinforcement learning in Markov decision processes, including partially observed cases.

Findings

Algorithms can be used as suboptimal methods for partially observed MDPs.

Provides a concise overview of stochastic approximation techniques in reinforcement learning.

Highlights potential applications in complex decision-making scenarios.

Abstract

This article presents a short and concise description of stochastic approximation algorithms in reinforcement learning of Markov decision processes. The algorithms can also be used as a suboptimal method for partially observed Markov decision processes.