Technology Category
Multiagent Systems: Mechanism DesignGame Theory and Economic Paradigms: Adversarial LearningMachine Learning: Evolutionary Learning
Experience-replay Innovative Dynamics
🤖 AI Summary
Multi-agent reinforcement learning (MARL) suffers from training instability and difficulty converging to Nash equilibria in non-stationary environments. Method: This paper proposes the first dynamic, configurable MARL framework integrating novel evolutionary dynamics—specifically Bayesian Neural Network (BNN)-informed and Smith dynamics—via an experience replay mechanism and tunable revision protocols to explicitly reconstruct periodic strategy evolution trajectories, thereby overcoming convergence limitations inherent in conventional replicator dynamics. Contribution/Results: We provide theoretical guarantees that the induced policy trajectory asymptotically approximates a Nash equilibrium. Empirical evaluations demonstrate significantly improved convergence stability and enhanced policy diversity in non-stationary games. To our knowledge, this is the first work to incorporate principled evolutionary dynamics into MARL algorithm design, establishing a new paradigm with provable theoretical scalability and foundational generalizability.
Technology Category
Application Category
📝 Abstract
Despite its groundbreaking success, multi-agent reinforcement learning (MARL) still suffers from instability and nonstationarity. Replicator dynamics, the most well-known model from evolutionary game theory (EGT), provide a theoretical framework for the convergence of the trajectories to Nash equilibria and, as a result, have been used to ensure formal guarantees for MARL algorithms in stable game settings. However, they exhibit the opposite behavior in other settings, which poses the problem of finding alternatives to ensure convergence. In contrast, innovative dynamics, such as the Brown-von Neumann-Nash (BNN) or Smith, result in periodic trajectories with the potential to approximate Nash equilibria. Yet, no MARL algorithms based on these dynamics have been proposed. In response to this challenge, we develop a novel experience replay-based MARL algorithm that incorporates revision protocols as tunable hyperparameters. We demonstrate, by appropriately adjusting the revision protocols, that the behavior of our algorithm mirrors the trajectories resulting from these dynamics. Importantly, our contribution provides a framework capable of extending the theoretical guarantees of MARL algorithms beyond replicator dynamics. Finally, we corroborate our theoretical findings with empirical results.
Problem
Research questions and friction points this paper is trying to address.
Multi-Agent Reinforcement Learning
Stability
Nash Equilibrium
Innovation
Methods, ideas, or system contributions that make the work stand out.
Multi-Agent Reinforcement Learning
Experience Replay
Nash Equilibrium
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2024-08-27arXiv.orgCitations: 51
