A Comparison between Deep Q-Networks and Deep Symbolic Reinforcement Learning

Deep Reinforcement Learning (DRL) has had several breakthroughs, from helicopter controlling and Atari games to the Alpha-Go success. Despite their success, DRL still lacks several important features of human intelligence, such as transfer learning, planning and interpretability. We compare two DRL approaches at learning and generalization: Deep Q-Networks and Deep Symbolic Reinforcement Learning. We implement simplified versions of these algorithms and propose two simple problems. Results indicate that although the symbolic approach is promising at generalizing and faster learning in one of the problems, it can fail systematically in the other, very similar problem.