Deep reinforcement learning has produced many success stories in recent years. Some
example fields in which these successes have taken place include mathematics, games …

Offline reinforcement learning leverages previously collected offline datasets to learn
optimal policies with no necessity to access the real environment. Such a paradigm is also …

With the breakthrough of AlphaGo, deep reinforcement learning has become a recognized
technique for solving sequential decision-making problems. Despite its reputation, data …

The necessity for cooperation among intelligent machines has popularised cooperative multi-
agent reinforcement learning (MARL) in AI research. However, many research endeavours …

Abstract We introduce the Multi-Agent Tracking Environment (MATE), a novel multi-agent
environment simulates the target coverage control problems in the real world. MATE hosts …

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 …

Many scenarios in mobility and traffic involve multiple different agents that need to cooperate
to find a joint solution. Recent advances in behavioral planning use Reinforcement Learning …

In numerous artificial intelligence applications, the collaborative efforts of multiple intelligent
agents are imperative for the successful attainment of target objectives. To enhance …

Reinforcement Learning (RL) is a potent tool for sequential decision-making and has
achieved performance surpassing human capabilities across many challenging real-world …

A significant challenge facing researchers in the area of multi-agent reinforcement learning
(MARL) pertains to the identification of a library that can offer fast and compatible …