Motivated by applications in large-scale and multi-agent reinforcement learning, we study
the non-asymptotic performance of stochastic approximation (SA) schemes with delayed …

Offline reinforcement learning (RL), which seeks to learn an optimal policy using offline data,
has garnered significant interest due to its potential in critical applications where online data …

Reinforcement learning has recently gained unprecedented popularity, yet it still grapples
with sample inefficiency. Addressing this challenge, federated reinforcement learning …

We consider a setting in which $ N $ agents aim to speedup a common Stochastic
Approximation (SA) problem by acting in parallel and communicating with a central server …

We explore a Federated Reinforcement Learning (FRL) problem where $ N $ agents
collaboratively learn a common policy without sharing their trajectory data. To date, existing …

Mean field games (MFGs) are a promising framework for modeling the behavior of large-
population systems. However, solving MFGs can be challenging due to the coupling of …

We consider a distributed setup for reinforcement learning, where each agent has a copy of
the same Markov Decision Process but transitions are sampled from the corresponding …