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A Survey on Interactive Reinforcement Learning: Design Principles and Open Challenges

arXiv 2105.12949 ↗First posted 27 May 2021cs.HCcited by 41

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Abstract (arXiv)

Interactive reinforcement learning (RL) has been successfully used in various applications in different fields, which has also motivated HCI researchers to contribute in this area. In this paper, we survey interactive RL to empower human-computer interaction (HCI) researchers with the technical background in RL needed to design new interaction techniques and propose new applications. We elucidate the roles played by HCI researchers in interactive RL, identifying ideas and promising research directions. Furthermore, we propose generic design principles that will provide researchers with a guide to effectively implement interactive RL applications.

Key facts. Cited by 41 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 May 2021.

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Citing papers per year: 2021: 3; 2022: 9; 2023: 7; 2024: 11; 2025: 7; 2026: 4.

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  1. 2022Robot Learning on the Job: Human-in-the-Loop Autonomy and Learning During Deploymentcited by 101
  2. 2025A Comprehensive Survey of Reward Models: Taxonomy, Applications, Challenges, and Futurecited by 53
  3. 2024TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correctioncited by 50
  4. 2022When to Ask for Help: Proactive Interventions in Autonomous Reinforcement Learningcited by 20
  5. 2023RLHF-Blender: A Configurable Interactive Interface for Learning from Diverse Human Feedbackcited by 15
  6. 2023A Human-Centered Safe Robot Reinforcement Learning Framework with Interactive Behaviorscited by 13
  7. 2022Redefining Counterfactual Explanations for Reinforcement Learning: Overview, Challenges and Opportunitiescited by 12
  8. 2021Interpretable Preference-based Reinforcement Learning with Tree-Structured Reward Functionscited by 10
  9. 2025Reward Learning from Multiple Feedback Typescited by 9
  10. 2023From "Thumbs Up" to "10 out of 10": Reconsidering Scalar Feedback in Interactive Reinforcement Learningcited by 6
  11. 2024Human-In-The-Loop Machine Learning for Safe and Ethical Autonomous Vehicles: Principles, Challenges, and Opportunitiescited by 6
  12. 2022Reinforcement Learning in Practice: Opportunities and Challengescited by 5
29 more
  1. 2024Mapping out the Space of Human Feedback for Reinforcement Learning: A Conceptual Frameworkcited by 5
  2. 2021Targeted Data Acquisition for Evolving Negotiation Agentscited by 3
  3. 2022Good Time to Ask: A Learning Framework for Asking for Help in Embodied Visual Navigationcited by 3
  4. 2024Medical Knowledge Integration into Reinforcement Learning Algorithms for Dynamic Treatment Regimescited by 3
  5. 2025From Noise to Signal to Selbstzweck: Reframing Human Label Variation in the Era of Post-training in NLPcited by 3
  6. 2021Towards Interactive Reinforcement Learning with Intrinsic Feedbackcited by 2
  7. 2022Teachable Reinforcement Learning via Advice Distillationcited by 2
  8. 2024How Much Progress Did I Make? An Unexplored Human Feedback Signal for Teaching Robotscited by 2
  9. 2025Interactive Explanations for Reinforcement-Learning Agentscited by 2
  10. 2025Grower-in-the-Loop Interactive Reinforcement Learning for Greenhouse Climate Controlcited by 2
  11. 2022Quantifying the Effect of Feedback Frequency in Interactive Reinforcement Learning for Robotic Taskscited by 1
  12. 2022Broad-persistent Advice for Interactive Reinforcement Learning Scenarioscited by 1
  13. 2023Establishing Shared Query Understanding in an Open Multi-Agent Systemcited by 1
  14. 2023Teach Me How to Learn: A Perspective Review towards User-centered Neuro-symbolic Learning for Robotic Surgical Systemscited by 1
  15. 2023Modifying RL Policies with Imagined Actions: How Predictable Policies Can Enable Users to Perform Novel Taskscited by 1
  16. 2024MEReQ: Max-Ent Residual-Q Inverse RL for Sample-Efficient Alignment from Interventioncited by 1
  17. 2022Incorporating Rivalry in Reinforcement Learning for a Competitive Gamecited by 0
  18. 2023ASQ-IT: Interactive Explanations for Reinforcement-Learning Agentscited by 0
  19. 2024Integrating Human Expertise in Continuous Spaces: A Novel Interactive Bayesian Optimization Framework with Preference Expected Improvementcited by 0
  20. 2024The Power of Combined Modalities in Interactive Robot Learningcited by 0
  21. 2024Imagining In-distribution States: How Predictable Robot Behavior Can Enable User Control Over Learned Policiescited by 0
  22. 2024Towards Interpretable Foundation Models of Robot Behavior: A Task Specific Policy Generation Approachcited by 0
  23. 2024Online Behavior Modification for Expressive User Control of RL-Trained Robotscited by 0
  24. 2025Can you see how I learn? Human observers' inferences about Reinforcement Learning agents' learning processescited by 0
  25. 2025"Stop replacing salt with sugar!'': Towards Intuitive Human-Agent Teachingcited by 0
  26. 2026Themis: An explainable AI-enabled framework for Reinforcement Learning with Human Feedbackcited by 0
  27. 2026Feedback Manipulation Regularization: Enabling Offline Agent Alignment for Imitation Learningcited by 0
  28. 2026Harnessing human expertise for high-precision robotic assembly in industrialized construction: A sample-efficient installer-in-the-loop interactive reinforcement learning frameworkcited by 0
  29. 2026PHIRL: Aligning Learned Rewards with Task Progress for Inverse Reinforcement Learningcited by 0