Multi-agent Reinforcement Learning (MARL) has gained wide attention in recent years and
has made progress in various fields. Specifically, cooperative MARL focuses on training a …

Commonly adopted in the manufacturing and aerospace sectors, digital twin (DT) platforms
are increasingly seen as a promising paradigm to control, monitor, and analyze software …

Benchmarks play an important role in the development of machine learning algorithms. For
example, research in reinforcement learning (RL) has been heavily influenced by available …

Training multiple agents to coordinate is an essential problem with applications in robotics,
game theory, economics, and social sciences. However, most existing Multi-Agent …

Game-based decision-making involves reasoning over both world dynamics and strategic
interactions among the agents. Typically, empirical models capturing these respective …

In mixed-incentive multi-agent environments, methods developed for zero-sum games often
yield collectively sub-optimal results. Addressing this,\textit {opponent shaping}(OS) …

Exploration under sparse rewards is a key challenge for multi-agent reinforcement learning
problems. Previous works argue that complex dynamics between agents and the huge …

Commonly adopted in the manufacturing and aerospace sectors, digital twin (DT) platforms
are increasingly seen as a promising paradigm to control and monitor software …

Abstract Training Reinforcement Learning (RL) policies for a robot requires an extensive
amount of data recorded while interacting with the environment. Acquiring such a policy on a …

In this article, we address a form of active perception characterized by curiosity-driven, open-
ended exploration with intrinsic motivation, carried out by a group of agents. The multiple …