Multi-Agent Planning in Adversarial Environments
Using Multi-Agent Reinforcement Learning for Planning and Coordination Operations in the Air Force.
My team at Multi AI has developed a multi-agent reinforcement learning platform that enables military operators and wargamers to quickly compute optimal, robust mission plans for large numbers of assets operating in adversarial environments, including robotic systems and military vehicles.
Why This Platform?
Developing “good” mission plans for large numbers of assets operating in adversarial environments is extremely challenging for military operators and wargamers. Traditional planning still relies on manual processes, which are slow and prone to errors. Our platform addresses this problem with a multi-agent planning tool that models the full complexity of the adversarial environments, including hostile activities and environmental uncertainties.
Key Features include:
- Digital Twin Simulation: Models real-world asset characteristics and operation processes and their sensitivity to disturbances and adversarial activities.
- AI Decision Support: Uses cutting-edge multi-agent reinforcement learning algorithms to analyze 1000s of what-if scenarios.
- Fast Computation: Mission plan computation for 100+ vehicles takes less than 5 minutes, instead of hours or days.
