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Agent-Agnostic Human-in-the-Loop Reinforcement Learning

arXiv 1701.04079 ↗First posted 15 Jan 2017cs.LG cs.AIcited by 23

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Abstract (arXiv)

Providing Reinforcement Learning agents with expert advice can dramatically improve various aspects of learning. Prior work has developed teaching protocols that enable agents to learn efficiently in complex environments; many of these methods tailor the teacher's guidance to agents with a particular representation or underlying learning scheme, offering effective but specialized teaching procedures. In this work, we explore protocol programs, an agent-agnostic schema for Human-in-the-Loop Reinforcement Learning. Our goal is to incorporate the beneficial properties of a human teacher into Reinforcement Learning without making strong assumptions about the inner workings of the agent. We show how to represent existing approaches such as action pruning, reward shaping, and training in simulation as special cases of our schema and conduct preliminary experiments on simple domains.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Jan 2017.

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Citing papers per year: 2018: 1; 2019: 0; 2020: 0; 2021: 3; 2022: 6; 2023: 4; 2024: 4; 2025: 4; 2026: 1.

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  1. 2018Scalable agent alignment via reward modeling: a research directioncited by 438
  2. 2024LLM-based Multi-Agent Reinforcement Learning: Current and Future Directionscited by 67
  3. 2022Efficient Learning of Safe Driving Policy via Human-AI Copilot Optimizationcited by 44
  4. 2021Safe Driving via Expert Guided Policy Optimizationcited by 29
  5. 2023Diagnosis, Feedback, Adaptation: A Human-in-the-Loop Framework for Test-Time Policy Adaptationcited by 16
  6. 2025Learning from Active Human Involvement through Proxy Value Propagationcited by 16
  7. 2021Recent Advances in Leveraging Human Guidance for Sequential Decision-Making Taskscited by 13
  8. 2024Preference-Conditioned Language-Guided Abstractioncited by 11
  9. 2021How RL Agents Behave When Their Actions Are Modifiedcited by 10
  10. 2023Guarded Policy Optimization with Imperfect Online Demonstrationscited by 10
  11. 2023Shattering the Agent-Environment Interface for Fine-Tuning Inclusive Language Modelscited by 8
  12. 2024Human-In-The-Loop Machine Learning for Safe and Ethical Autonomous Vehicles: Principles, Challenges, and Opportunitiescited by 6
11 more
  1. 2025Think Twice, Act Once: A Co-Evolution Framework of LLM and RL for Large-Scale Decision Makingcited by 5
  2. 2022DIAMBRA Arena: a New Reinforcement Learning Platform for Research and Experimentationcited by 4
  3. 2024Towards Sample-Efficiency and Generalization of Transfer and Inverse Reinforcement Learning: A Comprehensive Literature Reviewcited by 3
  4. 2025Data-Efficient Learning from Human Interventions for Mobile Robotscited by 3
  5. 2022Teachable Reinforcement Learning via Advice Distillationcited by 2
  6. 2022Human Decision Makings on Curriculum Reinforcement Learning with Difficulty Adjustmentcited by 2
  7. 2026Bounded Autonomy for Enterprise AI: Typed Action Contracts and Consumer-Side Executioncited by 1
  8. 2022A Reinforcement Learning-based Offensive semantics Censorship System for Chatbotscited by 0
  9. 2022Computational Charisma -- A Brick by Brick Blueprint for Building Charismatic Artificial Intelligencecited by 0
  10. 2023Constrained Exploration in Reinforcement Learning with Optimality Preservationcited by 0
  11. 2025Can you see how I learn? Human observers' inferences about Reinforcement Learning agents' learning processescited by 0