Study: swarm decision-making equivalent to single-agent RL

A new paper proves a mathematical equivalence between collective decision-making via local imitation (such as bees choosing a nest site) and single-agent reinforcement learning in a multi-armed bandit setting. It proposes Maynard-Cross Learning, in which N individuals act as N parallel rollouts with an effective step size of 1/N.