mlsanityData to 14 Oct 2026

Reinforcement Learning for Active Matter

arXiv 2503.23308 ↗First posted 30 Mar 2025cond-mat.soft cs.LG cs.ROcited by 4

On the map: Deep reinforcement learning methods → RL theory and sample complexity

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.

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.

Builds on

21 of 132 references.

References · 21

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2017Deep Reinforcement Learning: An Overviewcited by 333
  3. 2019A Theoretical Analysis of Deep Q-Learningcited by 215
  4. 2018Efficient collective swimming by harnessing vortices through deep reinforcement learningcited by 73
  5. 2019DeepGait: Planning and Control of Quadrupedal Gaits using Deep Reinforcement Learningcited by 50
  6. 2017Flow Navigation by Smart Microswimmers via Reinforcement Learningcited by 34
  7. 2019Machine learning strategies for path-planning microswimmers in turbulent flowscited by 14
  8. 2019A Comprehensive Review of Shepherding as a Bio-inspired Swarm-Robotics Guidance Approachcited by 8
  9. 2022Reinforcement learning of optimal active particle navigationcited by 6
  10. 2019Emergent Escape-based Flocking Behavior using Multi-Agent Reinforcement Learningcited by 5
  11. 2023Optimal foraging strategies can be learnedcited by 5
  12. 2019Learning to flock through reinforcementcited by 4
9 more
  1. 2022Combining imitation and deep reinforcement learning to accomplish human-level performance on a virtual foraging taskcited by 4
  2. 2024Counterfactual rewards promote collective transport using individually controlled swarm microrobotscited by 4
  3. 2022A Deep Reinforcement Learning Environment for Particle Robot Navigation and Object Manipulationcited by 3
  4. 2022Adaptive patch foraging in deep reinforcement learning agentscited by 3
  5. 2023Optimal active particle navigation meets machine learningcited by 3
  6. 2024Machine Learning Analysis of Anomalous Diffusioncited by 3
  7. 2022Programmable Control of Ultrasound Swarmbots through Reinforcement Learningcited by 1
  8. 2024Optimal navigation of magnetic artificial microswimmers in blood capillaries with deep reinforcement learningcited by 1
  9. 2024Learning to enhance multi-legged robot on rugged landscapescited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025FedRAIN-Lite: Federated Reinforcement Algorithms for Improving Idealised Numerical Weather and Climate Modelscited by 0
  2. 2025Data-Efficient Learning of Anomalous Diffusion with Wavelet Representations: Enabling Direct Learning from Experimental Trajectoriescited by 0
  3. 2026Reinforcement Learning Enables Autonomous Microrobot Navigation and Intervention in Simulated Blood Capillariescited by 0
  4. 2026Emergent aggregation from collective foragingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024SwarmRL: Building the Future of Smart Active Systems
  2. 2025Physical Embodiment Enables Information Processing Beyond Explicit Sensing in Active Matter ↗
  3. 2026Physics-Informed Modeling and Control of Emergent Behaviors in Robot Swarms ↗
  4. 2024Emergence of Chemotactic Strategies with Multi-Agent Reinforcement Learning
  5. 2019Bayesian policy selection using active inference
  6. 2025An effective control of large systems of active particles: An application to evacuation problem
  7. 2026Physical Imitation Learning: Distilling Control Policies into Passive Elasticity
  8. 2024Interacting Particle Systems for Fast Linear Quadratic RL