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cs.LG2026
Personalized Multi-Agent Average Reward TD-Learning via Joint Linear Approximation
Leo Muxing Wang, Pengkun Yang, Lili Su
We study personalized multi-agent average reward TD learning, in which a collection of agents interacts with different environments and jointly learns their respective value functi…
cs.LG2024
Learning with Shared Representations: Statistical Rates and Efficient Algorithms
Xiaochun Niu, Lili Su, Jiaming Xu +1
Collaborative learning through latent shared feature representations enables heterogeneous clients to train personalized models with improved performance and reduced sample complex…
cs.LG2024
On the Convergence Rates of Federated Q-Learning across Heterogeneous Environments
Leo Muxing Wang, Pengkun Yang, Lili Su
Large-scale multi-agent systems are often deployed across wide geographic areas, where agents interact with heterogeneous environments. There is an emerging interest in understandi…