# Reinforcement Learning and its Connections with Neuroscience and Psychology

arXiv: https://arxiv.org/abs/2007.01099 · HTML: https://mlsanity.com/paper/2007.01099/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2020.

## Built on by (most cited first, 7 of 7)

- [Using Artificial Populations to Study Psychological Phenomena in Neural Models](https://mlsanity.com/paper/2308.08032/) (2023)
- [Knowledge Transfer from Simple to Complex: A Safe and Efficient Reinforcement Learning Framework for Autonomous Driving Decision-Making](https://mlsanity.com/paper/2410.14468/) (2024)
- [Uncertainty Prioritized Experience Replay](https://mlsanity.com/paper/2506.09270/) (2025)
- [Of Mice and Machines: A Comparison of Learning Between Real World Mice and RL Agents](https://mlsanity.com/paper/2505.12204/) (2025)
- [Using Deep Reinforcement Learning for Zero Defect Smart Forging](https://mlsanity.com/paper/2201.10268/) (2022)
- [Combining Bayesian Inference and Reinforcement Learning for Agent Decision Making: A Review](https://mlsanity.com/paper/2505.07911/) (2025)
- [On Quantum Natural Policy Gradients](https://mlsanity.com/paper/2401.08307/) (2024)

## Abstract (arXiv)

Reinforcement learning methods have recently been very successful at performing complex sequential tasks like playing Atari games, Go and Poker. These algorithms have outperformed humans in several tasks by learning from scratch, using only scalar rewards obtained through interaction with their environment. While there certainly has been considerable independent innovation to produce such results, many core ideas in reinforcement learning are inspired by phenomena in animal learning, psychology and neuroscience. In this paper, we comprehensively review a large number of findings in both neuroscience and psychology that evidence reinforcement learning as a promising candidate for modeling learning and decision making in the brain. In doing so, we construct a mapping between various classes of modern RL algorithms and specific findings in both neurophysiological and behavioral literature. We then discuss the implications of this observed relationship between RL, neuroscience and psychology and its role in advancing research in both AI and brain science.