Covert Planning against Imperfect Observers

no code implementations • 25 Oct 2023 • Haoxiang Ma, Chongyang Shi, Shuo Han, Michael R. Dorothy, Jie Fu

This paper studies how covert planning can leverage the coupling of stochastic dynamics and the observer's imperfect observation to achieve optimal task performance without being detected.

Strategic Maneuver and Disruption with Reinforcement Learning Approaches for Multi-Agent Coordination

no code implementations • 17 Mar 2022 • Derrik E. Asher, Anjon Basak, Rolando Fernandez, Piyush K. Sharma, Erin G. Zaroukian, Christopher D. Hsu, Michael R. Dorothy, Thomas Mahre, Gerardo Galindo, Luke Frerichs, John Rogers, John Fossaceca

Reinforcement learning (RL) approaches can illuminate emergent behaviors that facilitate coordination across teams of agents as part of a multi-agent system (MAS), which can provide windows of opportunity in various military tasks.

reinforcement-learning Reinforcement Learning (RL)