Self-organizing reinforcement learning: a learner-free swarm approaches the explore-exploit optimum
The explore/exploit trade-off is the core problem of reinforcement learning (RL). We show that a minimal foraging swarm approaches its optimum - the ideal-free / Nash allocation of a multi-agent resource-sharing game (an equilibrium, not a return maximum) - with no learner and no reward signal. Agents explore by heavy-tailed (Levy) flights, recruit neighbours to a discovery through a threshold cascade on their proximity network, and disperse as a patch crowds; this cascade cycle balances exploration and exploitation and drives foragers toward the ideal free distribution. The swarm attains the shape of this optimum (committed foragers track richness with slope 0.94) but not its exact equil...
| Authors | Galen J. Wilkerson |
|---|---|
| Primary category | cs.LG |
| Published | 18 October 2026 |
| Last updated at source | 20 October 2026 |
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- 22 Oct 2026 Name: Self-organizing reinforcement learning: a swarm approaches the explore-exploit optimum → Self-organizing reinforcement learning: a learner-free swarm approaches the explore-exploit optimum
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