Reinforcement learning (RL) interacts with the environment to solve sequential decision-
making problems via a trial-and-error approach. Errors are always undesirable in real-world …

This book presents an overview of formal decision making methods for decentralized
cooperative systems. It is aimed at graduate students and researchers in the fields of …

Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has
led to a dramatic increase in the number of applications and methods. Recent works have …

Intrinsically motivated reinforcement learning aims to address the exploration challenge for
sparse-reward tasks. However, the study of exploration methods in transition-dependent …

The majority of multi-agent system implementations aim to optimise agents' policies with
respect to a single objective, despite the fact that many real-world problem domains are …

Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has
led to a dramatic increase in the number of applications and methods. Recent works have …

In domains such as electric vehicle charging, smart distribution grids and autonomous
warehouses, multiple agents share the same resources. When planning the use of these …

This paper deals with the problem of classical planning for multiple cooperative agents who
have private information about their local state and capabilities they do not want to reveal …

To optimally coordinate with others in cooperative games, it is often crucial to have
information about one's collaborators: successful driving requires understanding which side …

This article presents the state-of-the-art in optimal solution methods for decentralized
partially observable Markov decision processes (Dec-POMDPs), which are general models …