Explainable Reinforcement Learning with Counterfactuals and Causal Reasoning
Abstract
Explainable Reinforcement Learning (XRL) is an emerging field of research that attempts to provide human-understandable explanations for the decisions made by RL agents. Such agents often demonstrate complex behaviours, learned through many interactions with the environment, that their human counterparts may have not considered before. Being able to explain such complex behaviour in real-world crucial before they can responsibly be deployed on real-world problems. Reliable explanations can develop trust in the previously black-box policies of RL agents and enable effective interrogation of their behaviour in case of potential failure. By addressing the �why� and �why not�, �what if�, and �how� questions under human guidance, in this basic research project, we hope to provide better explanations from XRL and bring new opportunities for the use of models in various applications such as Unmanned Aerial Vehicle (UAV) route planning for C2 mission and intelligent driving.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Feb 16, 2024
- Source ID
- FA23862314066
Entities
People
- Flora Salim
Organizations
- Air Force Office of Scientific Research
- United States Air Force
- University of New South Wales