# Active Electrosensing and Communication in MARL-trained Weakly Electric Fish Collectives

arXiv: https://arxiv.org/abs/2511.08436 · HTML: https://mlsanity.com/paper/2511.08436/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 87 references that are in the map.
- First posted on arXiv 11 Nov 2025.

## Builds on (most cited first, 13 of 13)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games](https://mlsanity.com/paper/2103.01955/) (2021)
- [Emergent Multi-Agent Communication in the Deep Learning Era](https://mlsanity.com/paper/2006.02419/) (2020)
- [Gated Feedback Recurrent Neural Networks](https://mlsanity.com/paper/1502.02367/) (2015)
- [Deep neuroethology of a virtual rodent](https://mlsanity.com/paper/1911.09451/) (2019)
- [Machine learning strategies for path-planning microswimmers in turbulent flows](https://mlsanity.com/paper/1910.01728/) (2019)
- [Low-Bandwidth Communication Emerges Naturally in Multi-Agent Learning Systems](https://mlsanity.com/paper/2011.14890/) (2020)
- [InputDSA: Demixing then Comparing Recurrent and Externally Driven Dynamics](https://mlsanity.com/paper/2510.25943/) (2025)
- [Grounding Neuroscience in Behavioral Changes using Artificial Neural Networks](https://mlsanity.com/paper/2311.07526/) (2023)
- [Predicting the long-term collective behaviour of fish pairs with deep learning](https://mlsanity.com/paper/2302.06839/) (2023)
- [A framework for the emergence and analysis of language in social learning agents](https://mlsanity.com/paper/2305.02632/) (2023)
- [Emergent Dominance Hierarchies in Reinforcement Learning Agents](https://mlsanity.com/paper/2401.12258/) (2024)

## Abstract (arXiv)

How complex collective behavior emerges from individual interactions is a fundamental scientific question, but experimental cost and difficulty of simultaneous multi-brain recordings limit direct study in animals. Here we introduce a novel computational framework modeling weakly electric fish-like agents with biophysically inspired electrosensing and actuation, trained to forage collectively via multi-agent reinforcement learning (MARL). Trained agents reproduce hallmarks of real fish, including curvilinear homing trajectories and heavy-tailed electric organ discharge (EOD) interval statistics, while exhibiting emergent active sensing, social foraging, dominance-like asymmetries, and aggression. We perform in silico interventions including sensor ablations, EOD silencing, and food distribution changes to identify causal drivers of social foraging. Analyses of recurrent neural dynamics further show robust encoding of task-relevant variables and social context. Our work has broad implications for the neuroethology of weakly electric fish and other social animals where extensive multi-individual neural recordings, and thus traditional data-driven modeling, remain challenging.