A model for general video game learning with HTM (2016)
Abstract. A model-based agent, for general game playing in the context of Artificial General Intelligence is proposed. That agent is structured as an utility agent, that models the games through two functions, Transition and Reward. With these functions, a planning phase is proposed using a tree of actions. The agent is tested with two Atari games, Breakout and Pong.