39 citations · 89 across the 23 of their papers we have counts for
27 papers
Stochastic Approximation with Delayed Updates: Finite-Time Rates under Markovian Sampling
Arman Adibi, Nicolo Dal Fabbro, Luca Schenato +5
Motivated by applications in large-scale and multi-agent reinforcement learning, we study the non-asymptotic performance of stochastic approximation (SA) schemes with delayed updat…
DASA: Delay-Adaptive Multi-Agent Stochastic Approximation
Nicolò Dal Fabbro, Arman Adibi, H. Vincent Poor +3
We consider a setting in which agents aim to speedup a common Stochastic Approximation (SA) problem by acting in parallel and communicating with a central server. We assume tha…
Min-Max Optimization under Delays
Arman Adibi, Aritra Mitra, Hamed Hassani
Delays and asynchrony are inevitable in large-scale machine-learning problems where communication plays a key role. As such, several works have extensively analyzed stochastic opti…
Model-free Learning with Heterogeneous Dynamical Systems: A Federated LQR Approach
Han Wang, Leonardo F. Toso, Aritra Mitra +1
We study a model-free federated linear quadratic regulator (LQR) problem where M agents with unknown, distinct yet similar dynamics collaboratively learn an optimal policy to minim…
Federated TD Learning over Finite-Rate Erasure Channels: Linear Speedup under Markovian Sampling
Nicolò Dal Fabbro, Aritra Mitra, George J. Pappas
Federated learning (FL) has recently gained much attention due to its effectiveness in speeding up supervised learning tasks under communication and privacy constraints. However, w…
Federated Temporal Difference Learning with Linear Function Approximation under Environmental Heterogeneity
Han Wang, Aritra Mitra, Hamed Hassani +2
We initiate the study of federated reinforcement learning under environmental heterogeneity by considering a policy evaluation problem. Our setup involves agents interacting wi…