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What is reinforcement learning?

6 cited papers · March 16, 2026 · Powered by Researchly AI

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Reinforcement Learning (RL) is an AI paradigm in which an agent learns through interaction with an environment, learning from its mistakes and successes over ti…

Reinforcement Learning (RL) is an AI paradigm in which an agent learns through interaction with an environment, learning from its mistakes and successes over time.12Sallab et al. (2017)1

RL is considered the model-free counterpart of Dynamic Programming (DP), and dramatic developments in recent years have changed our understanding of what is possible with these methods. Buşoniu et al. (2010)

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Deep Reinforcement Learning framework for Autonomous DrivingAhmad EL Sallab, Mohammed Abdou et al.2017Electronic Imaging
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Continuous control for robot based on deep reinforcement learningShansi Zhang2019OpenAlex
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  • Reinforcement Learning (RL) — An AI learning paradigm where agents interact with an environment and learn from feedback, applicable to engineering, economics, medicine, and artificial intelligence.
1Buşoniu et al. (2010)2
  • Q-Learning — A foundational RL algorithm known to overestimate action values under certain conditions, which can harm performance in practice.
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  • Temporal Difference (TD) Learning — A foundational RL algorithm that has served as a workhorse for applied RL for nearly forty years and as a building block for more complex algorithms.
4Kim et al. (2025)4
  • Multi-Agent Reinforcement Learning (MARL) — An extension of single-agent RL to scenarios involving multiple agents, with critical issues including nonstationarity, scalability, and observability. Canese et al. (2021)
1
Deep Reinforcement Learning framework for Autonomous DrivingAhmad EL Sallab, Mohammed Abdou et al.2017Electronic Imaging
View
2
Reinforcement Learning and Dynamic Programming Using Function ApproximatorsLucian Buşoniu, Robert Babuška et al.2010OpenAlex
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3
Deep Reinforcement Learning with Double Q-LearningHado van Hasselt, Arthur Guez et al.2016Proceedings of the AAAI Conference on Artificial Intelligence
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4
Stabilizing Temporal Difference Learning via Implicit Stochastic RecursionHwanwoo Kim, Panos Toulis et al.2025arXiv
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Diagram
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| Environment |
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| State (s_t) ───→ Agent ───→ Action (a_t)
| |
| Reward (r_t) ←─┘
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