mlsanityData to 14 Oct 2026

A Deep Reinforcement Learning Framework for Closed-loop Guidance of Fish Schools via Virtual Agents

arXiv 2603.28200 ↗First posted 30 Mar 2026cs.RO cs.LG q-bio.PEcited by 0

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

Guiding collective motion in biological groups is a fundamental challenge in understanding social interaction rules. In this study, we propose a deep reinforcement learning (RL) framework for closed-loop guidance of fish schools using virtual agents. These agents are controlled by policies trained via Proximal Policy Optimization (PPO) in simulation and deployed in physical experiments with rummy-nose tetras (Petitella bleheri), enabling real-time interaction between artificial agents and live individuals. To cope with the stochastic behavior of live individuals, we designed a composite reward function that balances directional guidance with cohesion, providing a form of functional biomimicry at the level of the control objective. Our systematic evaluation of visual parameters showed that a white background and larger stimulus sizes produced the highest guidance efficacy among the tested conditions in physical trials. Furthermore, evaluation across group sizes and agent configurations indicated that guidance efficacy decreased as the group size increased from five to eight individuals, and that using multiple independently controlled agents did not improve guidance. Analysis of agent motion in the physical trials indicated that, under the learned policy, the agent moved toward the target while remaining close to the school and re-approached the school after advancing too far ahead. This study highlights the potential of deep RL for closed-loop guidance of fish schools and identifies challenges in maintaining artificial influence in larger groups.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 8 of its 54 references that are in the map. First posted on arXiv 30 Mar 2026.

Builds on

8 of 54 references.

Closest ancestors · 1

Same direction, newest first

  1. 2026Controlling Fish Schools via Reinforcement Learning of Virtual Fish Movement17 Mar

Other references · 7

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2021The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Gamescited by 942
  3. 2024YOLOv10: Real-Time End-to-End Object Detectioncited by 515
  4. 2018How to Blend a Robot within a Group of Zebrafish: Achieving Social Acceptance through Real-time Calibration of a Multi-level Behavioural Modelcited by 3
  5. 2023Predicting the long-term collective behaviour of fish pairs with deep learningcited by 3
  6. 2023Quantifying the biomimicry gap in biohybrid robot-fish pairscited by 1
  7. 2026Modeling and Analysis of Fish Interaction Networks under Projected Visual Stimulicited by 1

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2024Aquarium: A Comprehensive Framework for Exploring Predator-Prey Dynamics through Multi-Agent Reinforcement Learning Algorithms
  2. 2025Dissecting Larval Zebrafish Hunting using Deep Reinforcement Learning Trained RNN Agents
  3. 2024Deep Dive into Model-free Reinforcement Learning for Biological and Robotic Systems: Theory and Practice
  4. 2025Enhancing Efficiency and Propulsion in Bio-mimetic Robotic Fish through End-to-End Deep Reinforcement Learning
  5. 2025Active Electrosensing and Communication in MARL-trained Weakly Electric Fish Collectives
  6. 2024A computational approach to visual ecology with deep reinforcement learning
  7. 2025Data-driven simulator of multi-animal behavior with unknown dynamics via offline and online reinforcement learning
  8. 2024Aquatic Navigation: A Challenging Benchmark for Deep Reinforcement Learning