Proceedings of the Annual Meeting of the Cognitive Science Society

Deep reinforcement learning (RL) algorithms have recently achieved impressive results on a range of video games, learningto play them at or beyond a human level just from raw pixel inputs. However, do they leverage visual information in thesame manner as humans do? Our investigations suggest that they do not: given a static game, we find that a state-of-the-artdeep RL algorithm solves that game faster without visual input (only the agent location was provided to the algorithm).We posit that this is because deep RL attacks each problem tabula rasa, i.e. without any prior knowledge, as also suggestedby other recent work. We further propose that in certain settings, an agent is better off having no visual input comparedto having no visual priors. To demonstrate this, we conduct an experiment with human participants and find that peoplesolve a game that hid all visual input (except agent location) much faster than a game that prevented the use of variousvisual priors. These results highlight the importance of prior knowledge and provide a compelling demonstration of howthe lack of prior knowledge leads to deep RL algorithms approaching a problem very differently from humans.