Explaining decisions of a deep reinforcement learner with a cognitive architecture

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

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.

DOI