Bridging Swarm Intelligence and Reinforcement Learning

2024-10-23

arXiv.org

AI Summary

This paper investigates the theoretical equivalence between swarm intelligence (SI) collective decision-making and single-agent reinforcement learning (RL) in the multi-armed bandit (MAB) setting. Method: We propose “Maynard-Cross Learning”, a novel population-level RL update rule that unifies learning-rate adaptation and batch updating from a population-dynamics perspective for the first time; integrating opinion dynamics, evolutionary game theory, and RL theory into a cross-paradigm unified analytical framework. Contribution/Results: Our framework enables bidirectional methodological transfer: it yields interpretable, robust, distributed RL implementations while endowing SI with reward-driven dynamic optimization principles. The work reveals a deep conceptual consistency between SI and RL at the level of decision-making fundamentals, and provides new modeling tools and design principles for both paradigms—advancing theoretical understanding and practical algorithmic development in adaptive decision systems.

Technology Category

• Machine Learning: Online Learning & Bandits
• Game Theory and Economic Paradigms: Adversarial Learning
• Multiagent Systems: Multiagent Learning

Application Category

• Economics, Online Markets and Human Computation: Social networks and social learning
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Swarm intelligence (SI) explores how large groups of simple individuals (e.g., insects, fish, birds) collaborate to produce complex behaviors, exemplifying that the whole is greater than the sum of its parts. A fundamental task in SI is Collective Decision-Making (CDM), where a group selects the best option among several alternatives, such as choosing an optimal foraging site. In this work, we demonstrate a theoretical and empirical equivalence between CDM and single-agent reinforcement learning (RL) in multi-armed bandit problems, utilizing concepts from opinion dynamics, evolutionary game theory, and RL. This equivalence bridges the gap between SI and RL and leads us to introduce a novel abstract RL update rule called Maynard-Cross Learning. Additionally, it provides a new population-based perspective on common RL practices like learning rate adjustment and batching. Our findings enable cross-disciplinary fertilization between RL and SI, allowing techniques from one field to enhance the understanding and methodologies of the other.

Problem

• Swarm Intelligence
• Reinforcement Learning
• Multi-Armed Bandit Problem

Innovation

• Swarm Intelligence
• Reinforcement Learning
• Maynard-Cross Learning