mlsanityData to 7 Oct 2026

Introduction to Reinforcement Learning

arXiv 2408.07712 ↗First posted 13 Aug 2024cs.AI cs.LGcited by 1

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Abstract (arXiv)

Reinforcement Learning (RL), a subfield of Artificial Intelligence (AI), focuses on training agents to make decisions by interacting with their environment to maximize cumulative rewards. This paper provides an overview of RL, covering its core concepts, methodologies, and resources for further learning. It offers a thorough explanation of fundamental components such as states, actions, policies, and reward signals, ensuring readers develop a solid foundational understanding. Additionally, the paper presents a variety of RL algorithms, categorized based on the key factors such as model-free, model-based, value-based, policy-based, and other key factors. Resources for learning and implementing RL, such as books, courses, and online communities are also provided. By offering a clear, structured introduction, this paper aims to simplify the complexities of RL for beginners, providing a straightforward pathway to understanding.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 25 of its 58 references that are in the map. First posted on arXiv 13 Aug 2024.

Builds on

25 of 58 references.

References · 25

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  3. 2015Continuous control with deep reinforcement learningcited by 3,279
  4. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  5. 2015Trust Region Policy Optimizationcited by 2,503
  6. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,302
  7. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,218
  8. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,704
  9. 2015Prioritized Experience Replaycited by 1,070
  10. 2017Rainbow: Combining Improvements in Deep Reinforcement Learningcited by 774
  11. 2015Dueling Network Architectures for Deep Reinforcement Learningcited by 769
  12. 2017A Distributional Perspective on Reinforcement Learningcited by 743
13 more
  1. 2017A Brief Survey of Deep Reinforcement Learningcited by 466
  2. 2017Deep Reinforcement Learning: An Overviewcited by 327
  3. 2020QPLEX: Duplex Dueling Multi-Agent Q-Learningcited by 267
  4. 2024Reinforcement learningcited by 236
  5. 2018An Introduction to Deep Reinforcement Learningcited by 201
  6. 2022Exploration in Deep Reinforcement Learning: A Surveycited by 158
  7. 2017The Uncertainty Bellman Equation and Explorationcited by 81
  8. 2022A Survey on Model-based Reinforcement Learningcited by 65
  9. 2020Automated Lane Change Strategy using Proximal Policy Optimization-based Deep Reinforcement Learningcited by 30
  10. 2021Value Iteration in Continuous Actions, States and Timecited by 17
  11. 2019Deep Reinforcement Learning Attitude Control of Fixed-Wing UAVs Using Proximal Policy Optimizationcited by 14
  12. 2022Testing Stationarity and Change Point Detection in Reinforcement Learningcited by 12
  13. 2019Model-Free Reinforcement Learning for Financial Portfolios: A Brief Surveycited by 10

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Citing papers per year: 2024: 1.

1 paper.

  1. 2024One STEP at a time: Language Agents are Stepwise Plannerscited by 1