A Tutorial Introduction to Reinforcement Learning

Mathukumalli Vidyasagar
4/3/2023
cs.LGeess.SY

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Abstract

In this paper, we present a brief survey of Reinforcement Learning (RL), with particular emphasis on Stochastic Approximation (SA) as a unifying theme. The scope of the paper includes Markov Reward Processes, Markov Decision Processes, Stochastic Approximation algorithms, and widely used algorithms such as Temporal Difference Learning and $Q$-learning.

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Cite this paper

@article{vidyasagar2023tutorial,
  title  = {A Tutorial Introduction to Reinforcement Learning},
  author = {Mathukumalli Vidyasagar},
  year   = {2023},
  eprint = {2304.00803},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2304.00803v1}
}

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