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arXiv · 2102.09745

Decentralized Deterministic Multi-Agent Reinforcement Learning

Antoine Grosnit·Desmond Cai·Laura Wynter
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Abstract

[Zhang, ICML 2018] provided the first decentralized actor-critic algorithm for multi-agent reinforcement learning (MARL) that offers convergence guarantees. In that work, policies are stochastic and are defined on finite action spaces. We extend those results to offer a provably-convergent decentralized actor-critic algorithm for learning deterministic policies on continuous action spaces. Deterministic policies are important in real-world settings. To handle the lack of exploration inherent in deterministic policies, we consider both off-policy and on-policy settings. We provide the expression of a local deterministic policy gradient, decentralized deterministic actor-critic algorithms and convergence guarantees for linearly-approximated value functions. This work will help enable decentralized MARL in high-dimensional action spaces and pave the way for more widespread use of MARL.

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BibTeXRIS

Antoine Grosnit, Desmond Cai, Laura Wynter. 2021-02-19. Decentralized Deterministic Multi-Agent Reinforcement Learning. https://arxiv.org/abs/2102.09745

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