Interactive Machine Learning based on Deep Reinforcement Learning and Generative Adversarial Network Hybrid for Digital Twin
Abstract
A digital twin (DT) system should respond to unpredictable changes quickly which requires planning to be performed based on the real time operating conditions and dynamic changes to be handled with cognitive skills. Majority of existing works focus on fully autonomous ability but having human in the decision making loop is still critical especially in problems that are dynamic, inherit high complexity and the rules cannot be expressed explicitly. Existing learning approaches provide reliable and efficient real-time control and management of the system elements based on deep reinforcement learning (DRL) which requires huge training size. The performance of this approach depends on the distribution of the data in the learning environment.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- May 10, 2022
- Source ID
- FA23862114050XX0
Entities
People
- Sharef, N. M.
Organizations
- Air Force Office of Scientific Research
- United States Air Force
- University of Putra Malaysia