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Achieving Tighter Finite-Time Rates for Heterogeneous Federated Stochastic Approximation under Markovian Sampling
Feng Zhu, Aritra Mitra, Robert W. Heath
Motivated by collaborative reinforcement learning (RL) and optimization with time-correlated data, we study a generic federated stochastic approximation problem involving agent…
Adversarially-Robust TD Learning with Markovian Data: Finite-Time Rates and Fundamental Limits
Sreejeet Maity, Aritra Mitra
One of the most basic problems in reinforcement learning (RL) is policy evaluation: estimating the long-term return, i.e., value function, corresponding to a given fixed policy. Th…
Towards Fast Rates for Federated and Multi-Task Reinforcement Learning
Feng Zhu, Robert W. Heath, Aritra Mitra
We consider a setting involving agents, where each agent interacts with an environment modeled as a Markov Decision Process (MDP). The agents' MDPs differ in their reward funct…
Robust Q-Learning under Corrupted Rewards
Sreejeet Maity, Aritra Mitra
Recently, there has been a surge of interest in analyzing the non-asymptotic behavior of model-free reinforcement learning algorithms. However, the performance of such algorithms i…