Systems and methods for deep reinforcement learning using a brain-artificial intelligence interface

Abstract

The present disclosure relates to systems and methods for providing a hybrid brain-computer-interface (hBCI) that can detect an individual's reinforcement signals (e.g., level of interest, arousal, emotional reactivity, cognitive fatigue, cognitive state, or the like) in and/or response to objects, events, and/or actions in an environment by generating reinforcement signals for improving an AI agent controlling the environment, such as an autonomous vehicle. Although the disclosed subject matter is discussed within the context of an autonomous vehicle virtual reality game in the exemplary embodiments of the present disclosure, the disclosed system can be applicable to any other environment in which the human user's sensory input is to be used to influence actions within the environment. Furthermore, the systems and methods disclosed can use neural, physiological, or behavioral signatures to inform deep reinforcement learning based AI systems to enhance user comfort and trust in automation.