1 citations · 3 across the 13 of their papers we have counts for
6 papers · 1 filter
Learning from Unreliable Trajectories: Adversarially-Robust Federated Q-Learning
Sreejeet Maity, Aritra Mitra
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…
High-Probability Convergence of SGD via Batched Updates
Feng Zhu, Robert W. Heath, Aritra Mitra
Stochastic gradient descent (SGD) is the primary workhorse for large-scale optimization. While the average behavior of its iterates, typically characterized by mean-squared error b…
Robust Federated Q-Learning with Almost No Communication
Sreejeet Maity, Aritra Mitra
We consider a federated reinforcement learning setting involving agents, all of whom interact with a common Markov Decision Process (MDP). The agents exchange information via a…
Variance-Reduced Q-Learning over Static and Time-Varying Networks
Sreejeet Maity, Feng Zhu, Aritra Mitra +1
We investigate a decentralized reinforcement learning problem involving multiple agents that interact with the same Markov Decision Process (MDP). The agents can exchange informati…
Robust Asynchronous Q-Learning under Reward and State Corruption via Batching
Sreejeet Maity, Aritra Mitra
Motivated by reinforcement learning in harsh environments, we consider the problem of learning an optimal policy subject to adversarially corrupted feedback. Specifically, at each…
A Short and Unified Convergence Analysis of the SAG, SAGA, and IAG Algorithms
Feng Zhu, Robert W. Heath, Aritra Mitra
Stochastic variance-reduced algorithms such as Stochastic Average Gradient (SAG) and SAGA, and their deterministic counterparts like the Incremental Aggregated Gradient (IAG) metho…