Personalized Multi-Agent Average Reward TD-Learning via Joint Linear Approximation
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 functions. We focus on the setting where there exists a shared linear representation, and the agents' optimal weights collectively lie in an unknown linear subspace. Inspired by the recent success of personalized federated learning (PFL), we study the convergence of cooperative single-timescale TD learning in which agents iteratively estimate the common subspace and local heads. We showed that this decomposition can filter out conflicting signals, effectively mitigating the negative impacts of ``misaligned'' signals, ...
| Authors | Leo Muxing Wang, Pengkun Yang, Lili Su |
|---|---|
| Primary category | cs.LG |
| Published | 2 March 2026 |
| Last updated at source | 7 March 2026 |
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