mlsanityData to 7 Oct 2026

A Tutorial Introduction to Reinforcement Learning

arXiv 2304.00803 ↗First posted 3 Apr 2023cs.LG cs.SY eess.SYcited by 3

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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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 2 of its 48 references that are in the map. First posted on arXiv 3 Apr 2023.

Builds on

2 of 48 references.

References · 2

  1. 2021Finite-Sample Analysis of Off-Policy TD-Learning via Generalized Bellman Operatorscited by 17
  2. 2022Convergence of Stochastic Approximation via Martingale and Converse Lyapunov Methodscited by 8

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Cited by year

Citing papers per year: 2023: 1; 2024: 1; 2025: 1.

3 papers.

  1. 2023An Invitation to Deep Reinforcement Learningcited by 5
  2. 2024Combining Federated Learning and Control: A Surveycited by 2
  3. 2025A Deep Reinforcement Learning-Based TCP Congestion Control Algorithm: Design, Simulation, and Evaluationcited by 0