Toward a Psychology of Deep Reinforcement Learning Agents Using a Cognitive Architecture
Abstract
We argue that cognitive models can provide a common ground between human users and deep reinforcement learning (Deep RL) algorithms for purposes of explainable artificial intelligence (AI). Casting both the human and learner as cognitive models provides common mechanisms to compare and understand their underlying decision‐making processes. This common grounding allows us to identify divergences and explain the learner's behavior in human understandable terms. We present novel salience techniques that highlight the most relevant features in each model's decision‐making, as well as examples of this technique in common training environments such as Starcraft II and an OpenAI gridworld.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Sep 01, 2021
- Source ID
- 10.1111/tops.12573
Entities
People
- Christian Lebiere
- Joel Schooler
- Konstantinos Mitsopoulos
- Peter Pirolli
- Robert Thomson
- Sterling Somers
Organizations
- Air Force Research Laboratory
- Carnegie Mellon University
- Defense Advanced Research Projects Agency
- Florida Institute for Human and Machine Cognition