mlsanityData to 7 Oct 2026

Towards Interactive Reinforcement Learning with Intrinsic Feedback

arXiv 2112.01575 ↗First posted 2 Dec 2021cs.AI cs.HC cs.LGcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 36 of its 136 references that are in the map. First posted on arXiv 2 Dec 2021.

Builds on

36 of 136 references.

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24 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 0; 2026: 1.

2 papers.

  1. 2022Good Time to Ask: A Learning Framework for Asking for Help in Embodied Visual Navigationcited by 3
  2. 2026An offline approach to fNIRS-guided reinforcement learning for robot behaviorcited by 0