Investigating Human Priors for Playing Video Games

Abstract: Deep reinforcement learning algorithms have recently achieved impressive performance in playing video games. However, they require orders of magnitude more time than average human players to achieve the same performance. What makes humans so good at solving and figuring out these seemingly complex games? Here, we study one aspect critical to human decision making and problem solving – their use of strong priors (either learned or inbuilt), that helps them to generalize and solve tasks faster (as opposed to learning from scratch). Through systematic investigation of human performance in video games, we develop a taxonomy of different forms of prior knowledge employed by humans that enables them to quickly solve video games.