Reinforcement Learning as a Rehearsal for Planning in Air Battle Management

Abstract

Conduct fundamental research to advance Reinforcement Learning as a Rehearsal (RLaR) to Stratagem wargame, and disseminate the findings through publications in peer-reviewed venues, in collaboration/consultation with AFRL project team. Combine the results of research and a host of existing ideas to develop complete end-to-end deep learning RLaR based planners, one for each of the blue- and red-agents trained together as sparring partners, and deliver the code as well as the trained models to AFRL.

Document Details

Document Type
DoD Grant Award
Publication Date
Aug 11, 2021
Source ID
FA87502010105

Entities

People

  • Bikramjit Banjerjee

Organizations

  • Rome Laboratory
  • United States Air Force
  • University of Southern Mississippi

Tags

Readers

  • Military Training and Readiness Simulation
  • Neural Network Machine Learning.
  • Research Science/Academic Research

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy
  • AI & ML - Neural Networks