The next-generation of wireless networks will enable many machine learning (ML) tools and
applications to efficiently analyze various types of data collected by edge devices for …

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

We consider a distributed reinforcement learning setting where multiple agents separately
explore the environment and communicate their experiences through a central server …

We consider the problem where N agents collaboratively interact with an instance of a
stochastic K arm bandit problem for K> N. The agents aim to simultaneously minimize the …

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

In this paper, we consider federated reinforcement learning for tabular episodic Markov
Decision Processes (MDP) where, under the coordination of a central server, multiple …

We consider the concurrent reinforcement learning problem where $ n $ agents
simultaneously learn to make decisions in the same environment by sharing experience with …

Although parallelism has been extensively used in Reinforcement Learning (RL), the
quantitative effects of parallel exploration are not well understood theoretically. We study the …

Modern cyber-physical architectures use data col-lected from systems at different physical
locations to learn appropriate behaviors and adapt to uncertain environments. However, an …

In this paper, we consider model-free federated reinforcement learning for tabular episodic
Markov decision processes. Under the coordination of a central server, multiple agents …