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Federated Natural Policy Gradient and Actor Critic Methods for Multi-task Reinforcement Learning Tong Y ang

Neural Information Processing Systems

We further propose a federated natural actor critic (NAC) method for multi-task RL with function approximation and stochastic policy evaluation, and establish its finite-time sample complexity taking the errors of function approximation into account.