Learning from Unreliable Trajectories: Adversarially-Robust Federated Q-Learning
arXiv:2610.06918v1 Announce Type: new Abstract: We study federated reinforcement learning in which multiple agents interact with a common Markov decision process and communicate through a central server to collaboratively learn the optimal state-action value function. Our goal is to understand whet…
Read the full story at arXiv cs.LG ↗
Timeline · 1 report
- 2026-10-07 04:00 · arXiv cs.LG
Learning from Unreliable Trajectories: Adversarially-Robust Federated Q-Learning