Self-organizing reinforcement learning: a swarm approaches the explore-exploit optimum

Key points

• The explore/exploit trade-off is the core problem of reinforcement learning (RL).1
• 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.1
• The swarm attains the shape of this optimum (committed foragers track richness with slope 0.94) but not its exact equilibrium - it stays measurably exploitable - reaching it dynamically ( 150 steps) through the coupling of recruitment to movement: remove the coupling and matching collapses (0.94 -> 0.08).1
• In this one setting we read it not as beating RL but as a strong zero-shot, reward-free operating point: a demonstration that the self-organizing physics of a minimal swarm can carry a collective close to the optimum RL seeks, with none of RL's machinery.1

Sources (1)

• [1]Self-organizing reinforcement learning: a swarm approaches the explore-exploit optimum
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 18, 08:45 AM

  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.

  The explore/exploit trade-off is the core problem of reinforcement learning (RL).

Extractive summary: sentences quoted from the sources.