# Reinforcement Learning for Active Matter

arXiv: https://arxiv.org/abs/2503.23308 · HTML: https://mlsanity.com/paper/2503.23308/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in RL theory and sample complexity, one of 15 directions in Deep reinforcement learning methods.
- Builds on 21 of its 132 references that are in the map.
- First posted on arXiv 30 Mar 2025.

## Placed in

- Deep reinforcement learning methods / [RL theory and sample complexity](https://mlsanity.com/deeprl/rl-theory-and-sample-complexity/)

## Builds on (most cited first, 21 of 21)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Deep Reinforcement Learning: An Overview](https://mlsanity.com/paper/1701.07274/) (2017)
- [A Theoretical Analysis of Deep Q-Learning](https://mlsanity.com/paper/1901.00137/) (2019)
- [Efficient collective swimming by harnessing vortices through deep reinforcement learning](https://mlsanity.com/paper/1802.02674/) (2018)
- [DeepGait: Planning and Control of Quadrupedal Gaits using Deep Reinforcement Learning](https://mlsanity.com/paper/1909.08399/) (2019)
- [Flow Navigation by Smart Microswimmers via Reinforcement Learning](https://mlsanity.com/paper/1701.08848/) (2017)
- [Machine learning strategies for path-planning microswimmers in turbulent flows](https://mlsanity.com/paper/1910.01728/) (2019)
- [A Comprehensive Review of Shepherding as a Bio-inspired Swarm-Robotics Guidance Approach](https://mlsanity.com/paper/1912.07796/) (2019)
- [Reinforcement learning of optimal active particle navigation](https://mlsanity.com/paper/2202.00812/) (2022)
- [Emergent Escape-based Flocking Behavior using Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1905.04077/) (2019)
- [Optimal foraging strategies can be learned](https://mlsanity.com/paper/2303.06050/) (2023)
- [Learning to flock through reinforcement](https://mlsanity.com/paper/1911.01697/) (2019)
- [Combining imitation and deep reinforcement learning to accomplish human-level performance on a virtual foraging task](https://mlsanity.com/paper/2203.06250/) (2022)
- [Counterfactual rewards promote collective transport using individually controlled swarm microrobots](https://mlsanity.com/paper/2407.20041/) (2024)
- [A Deep Reinforcement Learning Environment for Particle Robot Navigation and Object Manipulation](https://mlsanity.com/paper/2203.06464/) (2022)
- [Adaptive patch foraging in deep reinforcement learning agents](https://mlsanity.com/paper/2210.08085/) (2022)
- [Optimal active particle navigation meets machine learning](https://mlsanity.com/paper/2303.05558/) (2023)
- [Machine Learning Analysis of Anomalous Diffusion](https://mlsanity.com/paper/2412.01393/) (2024)
- [Programmable Control of Ultrasound Swarmbots through Reinforcement Learning](https://mlsanity.com/paper/2209.15393/) (2022)
- [Optimal navigation of magnetic artificial microswimmers in blood capillaries with deep reinforcement learning](https://mlsanity.com/paper/2404.02171/) (2024)
- [Learning to enhance multi-legged robot on rugged landscapes](https://mlsanity.com/paper/2409.09473/) (2024)

## Built on by (most cited first, 4 of 4)

- [FedRAIN-Lite: Federated Reinforcement Algorithms for Improving Idealised Numerical Weather and Climate Models](https://mlsanity.com/paper/2508.14315/) (2025)
- [Data-Efficient Learning of Anomalous Diffusion with Wavelet Representations: Enabling Direct Learning from Experimental Trajectories](https://mlsanity.com/paper/2512.08510/) (2025)
- [Reinforcement Learning Enables Autonomous Microrobot Navigation and Intervention in Simulated Blood Capillaries](https://mlsanity.com/paper/2606.26154/) (2026)
- [Emergent aggregation from collective foraging](https://mlsanity.com/paper/2608.28046/) (2026)

## Abstract (arXiv)

Active matter refers to systems composed of self-propelled entities that consume energy to produce motion, exhibiting complex non-equilibrium dynamics that challenge traditional models. With the rapid advancements in machine learning, reinforcement learning (RL) has emerged as a promising framework for addressing the complexities of active matter. This review systematically introduces the integration of RL for guiding and controlling active matter systems, focusing on two key aspects: optimal motion strategies for individual active particles and the regulation of collective dynamics in active swarms. We discuss the use of RL to optimize the navigation, foraging, and locomotion strategies for individual active particles. In addition, the application of RL in regulating collective behaviors is also examined, emphasizing its role in facilitating the self-organization and goal-directed control of active swarms. This investigation offers valuable insights into how RL can advance the understanding, manipulation, and control of active matter, paving the way for future developments in fields such as biological systems, robotics, and medical science.