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

arXiv: https://arxiv.org/abs/2603.28200 · HTML: https://mlsanity.com/paper/2603.28200/

## 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.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 8 of 8)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games](https://mlsanity.com/paper/2103.01955/) (2021)
- [YOLOv10: Real-Time End-to-End Object Detection](https://mlsanity.com/paper/2405.14458/) (2024)
- [How to Blend a Robot within a Group of Zebrafish: Achieving Social Acceptance through Real-time Calibration of a Multi-level Behavioural Model](https://mlsanity.com/paper/1805.11371/) (2018)
- [Predicting the long-term collective behaviour of fish pairs with deep learning](https://mlsanity.com/paper/2302.06839/) (2023)
- [Quantifying the biomimicry gap in biohybrid robot-fish pairs](https://mlsanity.com/paper/2308.08978/) (2023)
- [Modeling and Analysis of Fish Interaction Networks under Projected Visual Stimuli](https://mlsanity.com/paper/2603.01682/) (2026)
- [Controlling Fish Schools via Reinforcement Learning of Virtual Fish Movement](https://mlsanity.com/paper/2603.16384/) (2026)

## 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.