Multi-agent fish simulation uses RNN agents with electric sensing and biting
tweetsatpreet · x · 2026-07-29
- The authors build a multi-agent reinforcement learning framework inspired by weakly electric fish.
- Each agent can move, emit pulses, and bite; rewards come from foraging, while bites incur penalties.
- The overview shows an environment with electric fields, recurrent actor-critic agents, and emergent behavior arising from the interaction of sensing, movement, and social competition.
More from Research
- Meta Paper: Optimizing Code Execution Speed via RL Achieves Up to 200% Improvement — facebook · 2026-07-30
- OPERA: Multi-Agent Framework for Biomedical Image Analysis Without Retraining — UW · 2026-07-30
- Nature: Medical AI is heading towards a reproducibility crisis — DrDatta_AIIMS · 2026-07-30
- Anthropic Tests Claude on Robotics: Direct Control Fails, LLM Supervision Hurts Familiar Tasks — DJiafei · 2026-07-30
- Building AI Employees: Engineering Autonomous Agents to Write Linux Utilities — leebase65 · 2026-07-30
- Breaking the Correctness-Efficiency Pareto Frontier in RLVR for Code — francoisfleuret · 2026-07-30