Federated reinforcement learning (FedRL) enables agents to collaboratively train a global
policy without sharing their individual data. However, high communication overhead …

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 …

Federated reinforcement learning (FRL) has emerged as a promising paradigm for reducing
the sample complexity of reinforcement learning tasks by exploiting information from …

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

To improve the efficiency of reinforcement learning, we propose a novel asynchronous
federated reinforcement learning framework termed AFedPG, which constructs a global …

Federated learning (FL) has recently gained much attention due to its effectiveness in
speeding up supervised learning tasks under communication and privacy constraints …

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

We consider the problem of federated Q-learning, where $ M $ agents aim to collaboratively
learn the optimal Q-function of an unknown infinite-horizon Markov decision process with …