Cognitive Architecture for Video Games

There has been an increasing interest in Frame-oriented reinforcement learning (FORL) in recent year. However, most of the works in the literature show little inspiration from human’s perception action reward cycle (PARC) and causation.Inspired by human’s vision system and learning strategy, we propose a novel architecture for FORL that understands the content of raw frames. The architecture achieves four objectives:1.Extracting information from the environment by exploiting only unsupervised learning and reinforcement learning.2.Understanding the content of a raw frame.3.Exploiting a Folvea vision strategy which is analogous to human’s vision system.4.Establishing self-awareness and collecting new training data subset automatically to learn new objects without forgetting previous ones..The architecture is developed in the Super Mario Brothers video game.. At first, Mario is the only object recognized by the architecture. After automatic data subset collection and memory update, the architecture can recognize both Goomba and Mario and classify them using incremental training.We exemplify performance of each piece of the architecture with snippets obtained from the video game.