Explaining decisions of a deep reinforcement learner with a cognitive architecture
Date
2018
Journal Title
Journal ISSN
Volume Title
Publisher
International Conference on Cognitive Modeling
Abstract
The work presented is an evaluation of a method for developing a hybrid system, consisting of a Deep Reinforcement Learning (RL) agent and a cognitive model, capable of providing explanations of its action decisions. The methodology uses a symbolic/sub-symbolic cognitive architecture to introspect on the activity of the network to understand its representation. The entropy in the system’s behavioral predictions could be used as a signal to affirm or deny ascribing a representation to the network.
Description
item.page.type
Conference presentations, papers, posters
item.page.format
Keywords
deep reinforcement learning, cognitive modeling, introspection
Citation
Somers, Sterling, Constantinos Mitsupoulos, Christian Lebiere, and Robert Thomson. "Explaining Decisions of a Deep Reinforcement Learner with a Cognitive Architecture." In Proceedings of the International Conference on Cognitive Modeling, 2018, 144-149.