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This document is a concise summary of many key results in single-agent reinforcement learning (RL). The intended audience are those who already have some familiarity with RL and are looking to review, reference and/or remind themselves of important ideas in the field.
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Introduction to Reinforcement Learning
Reinforcement Learning: An Overview
A Comprehensive Survey of Reinforcement Learning: From Algorithms to Practical Challenges
A General Perspective on Objectives of Reinforcement Learning
The Formalism-Implementation Gap in Reinforcement Learning Research
Understanding Reinforcement Learning Algorithms: The Progress from Basic Q-learning to Proximal Policy Optimization
@article{ahilan2023succinct,
title = {A Succinct Summary of Reinforcement Learning},
author = {Sanjeevan Ahilan},
year = {2023},
eprint = {2301.01379},
archivePrefix = {arXiv},
url = {http://arxiv.org/abs/2301.01379v1}
}Shows whether official code is linked for this paper.
[](https://codeofpaper.com/paper/2301.01379)