The interaction of multiple autonomous agents gives rise to highly dynamic and
nondeterministic environments, contributing to the complexity in applications such as …

Understanding the learning dynamics in multiagent systems is an important and challenging
task. Past research on multi-agent learning mostly focuses on two-agent settings. In this …

Modelling the dynamics of multi-agent learning has long been an important research topic,
but all of the previous works focus on 2-agent settings and mostly use evolutionary game …

Although learning has found wide application in multi-agent systems, its effects on the
temporal evolution of a system are far from understood. This paper focuses on the dynamics …

Understanding the evolutionary dynamics of reinforcement learning under multi-agent
settings has long remained an open problem. While previous works primarily focus on 2 …

Multi-agent learning is a challenging open task in artificial intelligence. It is known an
interesting connection between multi-agent learning algorithms and evolutionary game …

Imagine computer programs (agents) that learn to coordinate or to compete. This study
investigates how their learning processes influence each other. Such adaptive agents …

The theoretical underpinnings of multi-agent learning have recently attracted much attention.
In this paper, we study the learning dynamics of the softmax policy gradient (PG) algorithm in …

Modelling the dynamics of multi-agent reinforcement learning has long been an important
research topic. Most of the previous works focus on agents learning under global …

Este estudio tiene como objetivo analizar las principales propuestas teóricas y prácticas en
las que los agentes de software se han integrado con modelos de aprendizaje automático …