Multi-Agent Reinforcement Learning (MARL) is an emerging subfield of artificial intelligence that investigates how multiple autonomous agents can learn collaboratively and competitively within an ...

To enhance flexibility in timing plans, the periodic cycle is designed to include a maximum of four phases, allowing for a variety of phase combinations and durations to be explored. Specifically, the ...

Training standard AI models against a diverse pool of opponents — rather than building complex hardcoded coordination rules — is enough to produce cooperative multi-agent systems that adapt to each ...

ADELPHI, Md. — Army scientists developed an innovative method for evaluating the learned behavior of black-box Multi-Agent Reinforcement Learning, known as MARL, agents performing a pursuit-evasion ...

A recent study published in Engineering presents a significant advancement in manufacturing scheduling. Researchers Xueyan Sun, Weiming Shen, Jiaxin Fan, and their colleagues from Huazhong University ...

ADELPHI, Md. -- Army researchers developed a reinforcement learning approach that will allow swarms of unmanned aerial and ground vehicles to optimally accomplish various missions while minimizing ...

Reinforcement learning is a subfield of machine learning concerned with how an intelligent agent can learn through trial and error to make optimal decisions in its ...