Recent years have witnessed significant advances in reinforcement learning (RL), which
has registered tremendous success in solving various sequential decision-making problems …

Abstract Deep Reinforcement Learning has made significant progress in multi-agent
systems in recent years. The aim of this review article is to provide an overview of recent …

Actor-critic methods, a type of model-free Reinforcement Learning, have been successfully
applied to challenging tasks in continuous control, often achieving state-of-the art …

Policy gradient (PG) methods are popular reinforcement learning (RL) methods where a
baseline is often applied to reduce the variance of gradient estimates. In multi-agent RL …

Most policy search (PS) algorithms require thousands of training episodes to find an
effective policy, which is often infeasible with a physical robot. This survey article focuses on …

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 …

Many real-world tasks on practical control systems involve the learning and decision-making
of multiple agents, under limited communications and observations. In this paper, we study …

One of the fundamental issues for Federated Learning (FL) is data heterogeneity, which
causes accuracy degradation, slow convergence, and the communication bottleneck issue …

Deep deterministic policy gradient (DDPG) is a powerful reinforcement learning algorithm for
large-scale continuous controls. DDPG runs the back-propagation from the state-action …

Very often when studying non-equilibrium systems one is interested in analysing dynamical
behaviour that occurs with very low probability, so called rare events. In practice, since rare …