Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning
<|reference_start|>Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning: 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 $\widetilde{\mathcal{O}}(d^{3/2}H^2\sqrt{MK})$ regret bound with communication complexity $\widetilde{\mathcal{O}}(dHM^2)$, 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.<|reference_end|>
@article{hsu2024randomized,
    title={Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning},
    author={Hao-Lun Hsu, Weixin Wang, Miroslav Pajic, Pan Xu},
    journal={arXiv preprint arXiv:2404.10728},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.10728},
    primaryClass={cs.LG stat.ML}
}