Can swarms learn like RL agents?
Bridging Swarm Intelligence and Reinforcement Learning
October 24, 2024
https://arxiv.org/pdf/2410.17517This paper draws a novel link between swarm intelligence (SI) and reinforcement learning (RL) in the context of collective decision-making.
- It shows how a swarm using simple rules like the "voter rule" to reach consensus mirrors an RL agent learning via the "Cross Learning" update rule.
- It introduces "Maynard-Cross Learning", a new RL algorithm derived from analyzing how honeybees choose nesting sites.
- This connection offers a new perspective on RL techniques like learning rate adjustment and batching, framing them as reflections of swarm size and information aggregation.
For LLM-based multi-agent systems, this means that analyzing simple, decentralized interaction rules in swarms could inspire new, efficient learning algorithms and provide insights into optimizing large-scale LLM agent collaborations.