Cooperation in multi-agent learning (MAL) is a topic at the intersection of numerous
disciplines, including game theory, economics, social sciences, and evolutionary biology …

Emergent communication, or emergent language, is the field of research which studies how
human language-like communication systems emerge de novo in deep multi-agent …

Communication improves the efficiency and convergence of multiagent learning. Existing
study of agent communication has been limited on predefined fixed connections. While an …

Reaching consensus and convergence to equilibrium are two major challenges of multi-
agent systems. Although each has attracted significant attention, relatively few studies …

摘要强化学习(reinforcement learning, RL) 技术经历了数十年的发展, 已经被成功地应用于连续
决策的环境中. 如今强化学习技术受到越来越多的关注, 甚至被冠以最接近通用人工智能的方法 …

Traffic signal control (TSC) is a challenging problem within intelligent transportation systems
and has been tackled using multi-agent reinforcement learning (MARL). While centralized …

This paper is concerned with the problem of multi-UAV roundup inspired by hierarchical
cognition consistency learning based on an interaction mechanism. First, a dynamic …

Distributed intelligent systems (DIS) appear where natural intelligence agents (humans) and
artificial intelligence agents (algorithms) interact, exchanging data and decisions and …

Abstract Despite its successes, Deep Reinforcement Learning (DRL) yields non-
interpretable policies. Moreover, since DRL does not exploit symbolic relational …

Effective communication is crucial for the success of multi-agent systems, as it promotes
collaboration for attaining joint objectives and enhances competitive efforts towards …