usace.army.mil

Army researchers develop innovative framework for training AI

ADELPHI, Md. — Army researchers developed a pioneering framework that provides a baseline for the development of collaborative multi-agent systems. The framework is detailed in the survey paper ...
EurekAlert!

Offline pre-trained multi-agent decision transformer

Multi-agent reinforcement learning (MARL) algorithms play an essential role in solving complex decision-making tasks by learning from the interaction data between computerized agents and (simulated) ...
Nature

Multi-Agent Reinforcement Learning

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 ...
usace.army.mil

Army advances learning capabilities of drone swarms

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 ...
EurekAlert!

Deep reinforcement learning optimizes distributed manufacturing scheduling

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 ...
The Next Web

Reinforcement learning: How rewards create intelligent machines

In June 2021, scientists at the AI lab DeepMind made a controversial claim. The researchers suggested that we could reach artificial general intelligence (AGI) using one single approach: reinforcement ...
uni

Partitioning and Lifting in Multi-Agent Reinforcement Learning

Many real-life scenarios can be simplified and modeled using Markov decision processes (MDPs). These MDPs set an agent with available actions into a well-defined environment and let it act in there to ...