Building agents based on tree-search planning capabilities with learned models has
achieved remarkable success in classic decision-making problems, such as Go and Atari …

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 …

Multi-agent reinforcement learning (MARL) models multiple agents that interact and learn
within a shared environment. This paradigm is applicable to various industrial scenarios …

Multi-agent reinforcement learning (MARL) has recently excelled in solving challenging
cooperative and competitive multi-agent problems in various environments with, mostly, few …

Despite the fast development of multi-agent systems (MAS) and multi-agent reinforcement
learning (MARL) algorithms, there is a lack of unified evaluation platforms and commonly …

Abstract Model-based reinforcement learning (RL) has demonstrated remarkable successes
on a range of continuous control tasks due to its high sample efficiency. To save the …

Exploration is one of the central topic in reinforcement learning (RL). Many existing
approaches take a single agent perspective when tackling this problem. In this work, we …