Multi-agent reinforcement learning (MARL) has long been a significant research topic in
both machine learning and control systems. Recent development of (single-agent) deep …

Advances in communications and networking technologies are driving the computing
paradigm toward the end-edge-cloud collaborative architecture to leverage ubiquitous data …

In this paper, we study Federated Bandit, a decentralized Multi-Armed Bandit problem with a
set of N agents, who can only communicate their local data with neighbors described by a …

In this paper, we consider federated Q-learning, which aims to learn an optimal Q-function
by periodically aggregating local Q-estimates trained on local data alone. Focusing on …

Deep reinforcement learning has led to dramatic breakthroughs in the field of artificial
intelligence for the past few years. As the amount of rollout experience data and the size of …

Currently, most edge-based Deep Reinforcement Learning (Deep-RL) applications have
been deployed in the edge network, however, their mainstream studies are still short of …

We develop a mathematical framework for solving multi-task reinforcement learning (MTRL)
problems based on a type of policy gradient method. The goal in MTRL is to learn a common …

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

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

The nature-inspired behavior of collective motion is found to be an optimal solution in
swarming systems for predator avoidance and survival. In this work, we propose a two-level …