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20152026
most citedFinite-Time Analysis of Distributed TD(0) with Linear Function Approximation for Multi-Agent Reinforcement Learning

50 citations · 194 across the 41 of their papers we have counts for

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cs.MA2026

Internal State-Based Policy Gradient Methods for Partially Observable Markov Potential Games

Wonseok Yang, Thinh T. Doan

This letter studies multi-agent reinforcement learning in partially observable Markov potential games. Solving this problem is challenging due to partial observability, decentraliz…

cs.MA2022★ 2 cited

Convergence and Price of Anarchy Guarantees of the Softmax Policy Gradient in Markov Potential Games

Dingyang Chen, Qi Zhang, Thinh T. Doan

We study the performance of policy gradient methods for the subclass of Markov games known as Markov potential games (MPGs), which extends the notion of normal-form potential games…