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

Following the remarkable success of the AlphaGO series, 2019 was a booming year that
witnessed significant advances in multi-agent reinforcement learning (MARL) techniques …

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 large-scale multi-agent systems, the large number of agents and complex game
relationship cause great difficulty for policy learning. Therefore, simplifying the learning …

We study reward decomposition for explaining the decisions of reinforcement learning (RL)
agents. The approach decomposes rewards into sums of semantically meaningful reward …

Reinforcement learning Marco Wiering Martijn van Otterlo (Eds.) Reinforcement Learning
State-of-the-Art ADAPTATION, LEARNING, AND OPTIMIZATION Volume 12 123 Page 2 …

Nowadays, modern engineering systems require sophisticated maintenance strategies to
ensure their correct performance. Maintenance has become one of the most important tasks …

Multi-agent systems can be used to address problems in a variety of domains, including
robotics, distributed control, telecommunications, and economics. The complexity of many …

Multiagent systems are rapidly finding applications in a variety of domains, including
robotics, distributed control, telecommunications, and economics. The complexity of many …

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 …