Global Convergence Guarantees for Federated Policy Gradient Methods with
Adversaries
Global Convergence Guarantees for Federated Policy Gradient Methods with
Adversaries
Federated Reinforcement Learning (FRL) allows multiple agents to collaboratively build a decision making policy without sharing raw trajectories. However, if a small fraction of these agents are adversarial, it can lead to catastrophic results. We propose a policy gradient based approach that is robust to adversarial agents which can send …