A Tutorial Introduction to Reinforcement Learning
arXiv:2304.00803v112 citationsh-index: 55
Originality Synthesis-oriented
AI Analysis
This is an incremental tutorial for learners in the field, offering no new research contributions.
The paper provides a tutorial survey of Reinforcement Learning, focusing on Stochastic Approximation as a unifying theme to cover key concepts and algorithms like Markov Decision Processes and Q-learning.
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.