Papers › Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning

Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning

16 Apr 2024arXiv:2404.10728archive 2025-07-28

Hao-Lun Hsu, Weixin Wang, Miroslav Pajic, Pan Xu

We present the first study on provably efficient randomized exploration in cooperative multi-agent reinforcement learning (MARL). We propose a unified algorithm framework for randomized exploration in parallel Markov Decision Processes (MDPs), and two Thompson Sampling (TS)-type algorithms, CoopTS-PHE and CoopTS-LMC, incorporating the perturbed-history exploration (PHE) strategy and the Langevin Monte Carlo exploration (LMC) strategy respectively, which are flexible in design and easy to implement in practice. For a special class of parallel MDPs where the transition is (approximately) linear, we theoretically prove that both CoopTS-PHE and CoopTS-LMC achieve a 𝒪(d^(3/2)H²√(MK)) regret bound with communication complexity 𝒪(dHM²), where d is the feature dimension, H is the horizon length, M is the number of agents, and K is the number of episodes. This is the first theoretical result for randomized exploration in cooperative MARL. We evaluate our proposed method on multiple parallel RL environments, including a deep exploration problem (\textit{i.e.,} N-chain), a video game, and a real-world problem in energy systems. Our experimental results support that our framework can achieve better performance, even under conditions of misspecified transition models. Additionally, we establish a connection between our unified framework and the practical application of federated learning.

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