Understanding Individual Decision-Making in Multi-Agent Reinforcement Learning

This paper proposes a novel dynamical systems approach to analyze individual decision-making in Multi-Agent Reinforcement Learning (MARL) environments, capturing both agent interactions and environmental characteristics.

Details

The paper addresses the challenge of analyzing individual decision-making in Multi-Agent Reinforcement Learning (MARL) environments, which is crucial for practical deployments with strict safety requirements. Traditional analytical approaches like replicator dynamics often rely on mean-field approximations to remove stochastic effects, but this simplification can lead to a disconnect between analytical predictions and actual realizations of individual agent trajectories. The authors propose a novel perspective by modeling MARL systems as coupled stochastic dynamical systems, capturing both agent interactions and environmental characteristics. Leveraging tools from dynamical systems theory, the framework allows for a rigorous study of MARL dynamics, taking into consideration the inherent stochasticity, to provide a deeper understanding of system behavior and practical insights for the design and control of multi-agent learning processes.