Reinforcement learning (RL) and brain-computer interfaces (BCI) have experienced significant growth over the past decade. With rising interest in human-in-the-loop (HITL), incorporating human input with RL algorithms has given rise to the sub-field of interactive RL. Adjacently, the field of BCI has long been interested in extracting informative brain signals from neural activity for use in human-computer interactions. A key link between these fields lies in the interpretation of neural activity as feedback such that interactive RL approaches can be employed. We denote this new and emerging medium of feedback as intrinsic feedback. Despite intrinsic feedback's ability to be conveyed automatically and even unconsciously, proper exploration surrounding this key link has largely gone unaddressed by both communities. Thus, to help facilitate a deeper understanding and a more effective utilization, we provide a tutorial-style review covering the motivations, approaches, and open problems of intrinsic feedback and its foundational concepts.

翻译：强化学习（RL）和脑机接口（BCI）在过去十年中取得了显著进展。随着人机协同（HITL）日益受到关注，将人类输入与强化学习算法相结合催生了交互式强化学习这一子领域。与此同时，脑机接口领域长期以来一直致力于从神经活动中提取可用于人机交互的有效脑信号。这两个领域之间的关键联系在于将神经活动解释为反馈信号，从而能够采用交互式强化学习方法。我们称这种新兴的反馈媒介为内在反馈。尽管内在反馈能够自动甚至无意识地传递，但两个研究领域对于这一关键联系的深入探索仍基本处于空白状态。为促进更深层次的理解和更有效的应用，本文以教程形式系统回顾了内在反馈及其基础概念的研究动机、方法体系及开放性问题。