Multi-Armed Bandits

How Swarm Imitation Mirrors a Single Agent Learning from Rewards

Honey bees don't learn, yet their hive collectively solves complex problems. This paper proves that blind imitation, where individuals simply copy neighbors without any personal trial-and-error, mathematically mirrors a single-agent reinforcement learning update. The swarm's population distribution evolves exactly like a multi-armed bandit algorithm, with each bee serving as a parallel environment sample. It's a formal mechanism for how "hive minds" work, and it opens practical doors for decentralized robotics. For more on the fundamentals, explore our guide to multi-armed bandit simulations in Python.