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 task that provides a baseline for the development of adaptive multi-agent systems, known as MAS.
This state-of-the-art research is detailed in the paper, “Emergent behaviors in multi-agent target acquisition”, which is featured in the SPIE Digital Library. The researchers said that this work addressed the Army modernization priority to develop Artificial Intelligence, known as AI, systems that can flexibly adapt to the human Soldier and the surrounding environment.
The researchers said that mission success would depend on sufficient coordination between AI systems and the Soldier enabling AI systems with critical action selection capabilities to provide the Soldier with improved safety, enhanced situational awareness, and general force multiplication. Researchers expect that such AI systems will be able to adapt to specific learning experiences to entirely new situations by adhering to the military operations Observe, Orient, Decide, and Act (OODA) loop.