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About this paper
Jiechuan Jiang, Kefan Su, Zongqing Lu
A comprehensive survey of fully decentralized cooperative Multi-Agent Reinforcement Learning (MARL) methods reveals how various algorithms address the non-stationarity problem to achieve convergence or equilibrium in shared and reward-sum settings, identifying key challenges and future research directions for truly autonomous multi-agent systems.
Key insights
- The non-stationarity problem is the central challenge in fully decentralized MARL, arising from independent agent updates and requiring specialized algorithmic approaches for convergence.
- While some value-based methods, like BQL, can converge to optimal joint policies in stochastic environments, current policy-based methods in fully decentralized settings often only guarantee convergence to suboptimal policies or specific equilibria.
- Significant challenges remain in fully decentralized MARL, including improving sample efficiency, achieving coordination without explicit communication, handling partial observability, and developing robust offline learning paradigms.
Summary by alphaXiv.
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