SymDQN: Symbolic Knowledge and Reasoning in Neural Network-based Reinforcement Learning

Ivo Amador, Nina Gierasimczuk

We propose a learning architecture that allows symbolic control and guidance in reinforcement learning with deep neural networks. We introduce SymDQN, a novel modular approach that…