Transformers, originally devised for natural language processing (NLP), have also produced
significant successes in computer vision (CV). Due to their strong expression power …

Self-adaptive systems (SASs) are designed to handle changes and uncertainties through a
feedback loop with four core functionalities: monitoring, analyzing, planning, and execution …

Deep generative models (DGMs) have demonstrated great success across various domains,
particularly in generating texts, images, and videos using models trained from offline data …

Reinforcement learning (RL) has become a dominant decision-making paradigm and has
achieved notable success in many real-world applications. Notably, deep neural networks …

Multi-agent systems are characterized by environmental uncertainty, varying policies of
agents, and partial observability, which result in significant risks. In the context of Multi-Agent …

The popularity of multiagent reinforcement learning (MARL) is growing rapidly with the
demand for real-world tasks that require swarm intelligence. However, a noticeable …

Multi-Agent Reinforcement Learning (MARL) has recently emerged as a significant area of
research. However, MARL evaluation often lacks systematic diversity, hindering a …

Multi-agent reinforcement learning (MARL) demonstrates significant progress in solving
cooperative and competitive multi-agent problems in various environments. One of the …

In cooperative multi-agent reinforcement learning (MARL), the environmental stochasticity
and uncertainties will increase exponentially when the number of agents increases, which …

ABSTRACT In Reinforcement Learning, deep neural networks play a crucial role, especially
in Multi-Agent Systems. Owing to information from multiple sources, the challenge lies in …