US Army Researchers Develop A New Framework For Collaborative Multi-Agent Reinforcement Learning Systems
Centralized learning for multi-agent systems highly depends on information-sharing mechanisms. However, there have not been significant studies within the research community in this domain.
Army researchers collaborate to propose a framework that provides a baseline for the development of collaborative multi-agent systems. The team involved Dr. Piyush K. Sharma, Drs. Erin Zaroukian, Rolando Fernandez, Derrik Asherat, Michael Dorothy from DEVCOM, Army Research Laboratory, and Anjon Basak, a postdoctoral fellow from the Oak Ridge Associated Universities fellowship program.
The team’s survey in reinforcement learning (RL) algorithms and their information sharing paradigms serves as a basis to question centralized learning for multi-agent systems that would improve their ability to work together.
Studies show that training various agents together is quite challenging. This is because the dynamic nature of complex environments suffers from dimensionality. So, increasing the number of agents while training can complicate the coordination. Moreover, information-sharing parameters are confusing and difficult to understand.
