Reinforcement learning (RL) and brain-computer interfaces (BCI) are two fields that have been growing over the past decade. Until recently, these fields have operated independently of one another. With the rising interest in human-in-the-loop (HITL) applications, RL algorithms have been adapted to account for human guidance giving rise to the sub-field of interactive reinforcement learning (IRL). Adjacently, BCI applications have been long interested in extracting intrinsic feedback from neural activity during human-computer interactions. These two ideas have set RL and BCI on a collision course for one another through the integration of BCI into the IRL framework where intrinsic feedback can be utilized to help train an agent. This intersection has been denoted as intrinsic IRL. To further help facilitate deeper ingratiation of BCI and IRL, we provide a review of intrinsic IRL with an emphasis on its parent field of feedback-driven IRL while also providing discussions concerning the validity, challenges, and future research directions.

翻译：强化学习(RL)和大脑-计算机界面(BCI)是过去十年来不断增长的两个领域,直到最近,这两个领域一直相互独立运作。随着对环形人(HITL)应用程序的兴趣日益浓厚,RL算法已经调整,以顾及人的指导,从而导致互动强化学习(IRL)的子领域。相邻,BCI应用程序长期以来都有兴趣在人-计算机互动期间从神经活动中提取内在反馈。这两个想法通过将BCI纳入IRL框架,使RL和BCI相互碰撞,利用内在反馈来帮助培训一个代理。这一交叉法被描述为内在的IRL。为了进一步帮助更深入地促进BCI和IRL的偏爱,我们审查了内在的IRL,重点是由反馈驱动的IRL的母领域,同时就有效性、挑战和未来研究方向展开讨论。