8 papers
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…
Corruption-Tolerant Asynchronous Q-Learning with Near-Optimal Rates
Sreejeet Maity, Aritra Mitra
We study the problem of learning the optimal policy in a discounted, infinite-horizon reinforcement learning (RL) setting in the presence of adversarially corrupted rewards. To add…
Outlier-Robust Linear System Identification Under Heavy-tailed Noise
Vinay Kanakeri, Aritra Mitra
We consider the problem of estimating the state transition matrix of a linear time-invariant (LTI) system, given access to multiple independent trajectories sampled from the system…
Boosting-Enabled Robust System Identification of Partially Observed LTI Systems Under Heavy-Tailed Noise
Vinay Kanakeri, Aritra Mitra
We consider the problem of system identification of partially observed linear time-invariant (LTI) systems. Given input-output data, we provide non-asymptotic guarantees for identi…