# A Tutorial Introduction to Reinforcement Learning

arXiv: https://arxiv.org/abs/2304.00803 · HTML: https://mlsanity.com/paper/2304.00803/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 48 references that are in the map.
- First posted on arXiv 3 Apr 2023.

## Builds on (most cited first, 2 of 2)

- [Finite-Sample Analysis of Off-Policy TD-Learning via Generalized Bellman Operators](https://mlsanity.com/paper/2106.12729/) (2021)
- [Convergence of Stochastic Approximation via Martingale and Converse Lyapunov Methods](https://mlsanity.com/paper/2205.01303/) (2022)

## Built on by (most cited first, 3 of 3)

- [An Invitation to Deep Reinforcement Learning](https://mlsanity.com/paper/2312.08365/) (2023)
- [Combining Federated Learning and Control: A Survey](https://mlsanity.com/paper/2407.11069/) (2024)
- [A Deep Reinforcement Learning-Based TCP Congestion Control Algorithm: Design, Simulation, and Evaluation](https://mlsanity.com/paper/2508.01047/) (2025)

## Abstract (arXiv)

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.