1 citations · 3 across the 13 of their papers we have counts for
5 papers · 1 filter
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…
Finite-Time Analysis of Asynchronous Multi-Agent TD Learning
Nicolò Dal Fabbro, Arman Adibi, Aritra Mitra +1
Recent research endeavours have theoretically shown the beneficial effect of cooperation in multi-agent reinforcement learning (MARL). In a setting involving agents, this benef…
A Simple Finite-Time Analysis of TD Learning with Linear Function Approximation
Aritra Mitra
We study the finite-time convergence of TD learning with linear function approximation under Markovian sampling. Existing proofs for this setting either assume a projection step in…
Finite-Time Analysis of On-Policy Heterogeneous Federated Reinforcement Learning
Chenyu Zhang, Han Wang, Aritra Mitra +1
Federated reinforcement learning (FRL) has emerged as a promising paradigm for reducing the sample complexity of reinforcement learning tasks by exploiting information from differe…