A Tutorial Introduction to Reinforcement Learning

2023/04/03 by M. Vidyasagar, Vidyasagar, Mathukumalli · 1 citation
Engineering · #Advanced Research in Systems and Signal Processing #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2304.00803

openalex publication_date 2023/04/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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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