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

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

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 …

Federated Reinforcement Learning (FedRL) encourages distributed agents to learn
collectively from each other's experience to improve their performance without exchanging …

We initiate the study of federated reinforcement learning under environmental heterogeneity
by considering a policy evaluation problem. Our setup involves $ N $ agents interacting with …

Federated reinforcement learning (RL) enables collaborative decision making of multiple
distributed agents without sharing local data trajectories. In this work, we consider a multi …

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

The growing literature of Federated Learning (FL) has recently inspired Federated
Reinforcement Learning (FRL) to encourage multiple agents to federatively build a better …

Cooperative multi-agent reinforcement learning is a decentralized paradigm in sequential
decision making where agents distributed over a network iteratively collaborate with …

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