From Optimal Policies to Individual Differences: Rethinking Reinforcement Learning for Biology
On the map: Deep reinforcement learning methods → RL for games and combinatorial optimization
Abstract (arXiv)
Reinforcement learning (RL) is primarily known as a computational method for optimizing control tasks, but it is increasingly used to explain biological behavior. While RL successfully captures key aspects of biology, a major gap remains: between-agent behavioral variability. Consistent individual differences naturally permeate biological populations, yet RL models typically present only the single best individual or the population average. Addressing this gap requires moving beyond current practices to generate behavioral diversity using biologically plausible mechanisms. Here, we examine approaches from various subfields of RL and outline potential paths forward to close the gap between biology and simulation.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods. Builds on 22 of its 56 references that are in the map. First posted on arXiv 17 Jul 2026.
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22 of 56 references.
References · 22
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- 2018Deep Online Learning via Meta-Learning: Continual Adaptation for Model-Based RLcited by 81
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10 more
- 2021The Option Keyboard: Combining Skills in Reinforcement Learningcited by 67
- 2019Distributional Reinforcement Learning for Efficient Explorationcited by 60
- 2019Learning Transferable Cooperative Behavior in Multi-Agent Teamscited by 44
- 2024AdaSociety: An Adaptive Environment with Social Structures for Multi-Agent Decision-Makingcited by 20
- 2021Towards a Theory of Evolution as Multilevel Learningcited by 18
- 2019Let's Play Again: Variability of Deep Reinforcement Learning Agents in Atari Environmentscited by 11
- 2022The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learningcited by 10
- 2023Emergent collective intelligence from massive-agent cooperation and competitioncited by 4
- 2023Habits and goals in synergy: a variational Bayesian framework for behaviorcited by 4
- 2025Depression as a disorder of distributional codingcited by 1
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- 2024Ancestral Reinforcement Learning: Unifying Zeroth-Order Optimization and Genetic Algorithms for Reinforcement Learning
- 2025World Models Unlock Optimal Foraging Strategies in Reinforcement Learning Agents
- 2024Emergence of Chemotactic Strategies with Multi-Agent Reinforcement Learning
- 2024Aquarium: A Comprehensive Framework for Exploring Predator-Prey Dynamics through Multi-Agent Reinforcement Learning Algorithms
- 2026Reinforcement Learning in the Real World: A Survey of Statistical Challenges and Future Directions
- 2024Evolution of Societies via Reinforcement Learning