Part of Industry 4.0 is building computer systems by combining artificial intelligence with
robotics. Such computer systems play an important role in the planning of cargo …

In this review, we present an analysis of the most used multi-agent reinforcement learning
algorithms. Starting with the single-agent reinforcement learning algorithms, we focus on the …

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 …

Multi-agent systems are rapidly finding applications in a variety of domains, including
robotics, distributed control, telecommunications, etc. Although the individual agents can be …

Reinforcement learning (RL) is a learning technique that provides a means for learning an
optimal control policy when the dynamics of the environment under consideration is …

This article addresses weighting and partitioning, in complex reinforcement learning tasks,
with the aim of facilitating learning. The article presents some ideas regarding weighting of …

In the context of reinforcement learning, transfer learning refers to the concept of reusing
knowledge acquired in past tasks to speed up the learning procedure in new tasks. Transfer …

Some researchers have introduced transfer learning mechanisms to multiagent
reinforcement learning (MARL). However, the existing works devoted to cross-task transfer …

This work presents a new algorithm, called Heuristically Accelerated Minimax-Q (HAMMQ),
that allows the use of heuristics to speed up the wellknown Multiagent Reinforcement …