Learning from Unreliable Trajectories: Adversarially-Robust Federated Q-Learning

Excerpt

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 whether the sample-efficiency benefits of collaboration can be retained when a fraction of the agents behave adversarially and transmit arbitrarily corrupted information. To address this problem, we introd