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Leveraging Human Guidance for Deep Reinforcement Learning Tasks

arXiv 1909.09906 ↗First posted 21 Sep 2019cs.AI cs.LGcited by 42

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Abstract (arXiv)

Reinforcement learning agents can learn to solve sequential decision tasks by interacting with the environment. Human knowledge of how to solve these tasks can be incorporated using imitation learning, where the agent learns to imitate human demonstrated decisions. However, human guidance is not limited to the demonstrations. Other types of guidance could be more suitable for certain tasks and require less human effort. This survey provides a high-level overview of five recent learning frameworks that primarily rely on human guidance other than conventional, step-by-step action demonstrations. We review the motivation, assumption, and implementation of each framework. We then discuss possible future research directions.

Key facts. Cited by 42 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Sep 2019.

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Citing papers per year: 2020: 4; 2021: 8; 2022: 10; 2023: 5; 2024: 8; 2025: 4; 2026: 3.

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  1. 2021What Matters in Learning from Offline Human Demonstrations for Robot Manipulationcited by 699
  2. 2021A Survey of Human-in-the-loop for Machine Learningcited by 214
  3. 2020Human-in-the-Loop Imitation Learning using Remote Teleoperationcited by 103
  4. 2022Robot Learning on the Job: Human-in-the-Loop Autonomy and Learning During Deploymentcited by 101
  5. 2024TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correctioncited by 50
  6. 2020The EMPATHIC Framework for Task Learning from Implicit Human Feedbackcited by 38
  7. 2022A Comprehensive Survey of Data Augmentation in Visual Reinforcement Learningcited by 23
  8. 2024Task Success is not Enough: Investigating the Use of Video-Language Models as Behavior Critics for Catching Undesirable Agent Behaviorscited by 23
  9. 2022Leveraging Approximate Symbolic Models for Reinforcement Learning via Skill Diversitycited by 22
  10. 2024Uni-RLHF: Universal Platform and Benchmark Suite for Reinforcement Learning with Diverse Human Feedbackcited by 20
  11. 2023Diagnosis, Feedback, Adaptation: A Human-in-the-Loop Framework for Test-Time Policy Adaptationcited by 16
  12. 2023Primitive Skill-based Robot Learning from Human Evaluative Feedbackcited by 15
30 more
  1. 2021Recent Advances in Leveraging Human Guidance for Sequential Decision-Making Taskscited by 13
  2. 2023NOIR: Neural Signal Operated Intelligent Robots for Everyday Activitiescited by 8
  3. 2022Relative Behavioral Attributes: Filling the Gap between Symbolic Goal Specification and Reward Learning from Human Preferencescited by 7
  4. 2020Efficiently Guiding Imitation Learning Agents with Human Gazecited by 6
  5. 2024Selective Exploration and Information Gathering in Search and Rescue Using Hierarchical Learning Guided by Natural Language Inputcited by 6
  6. 2024Mapping out the Space of Human Feedback for Reinforcement Learning: A Conceptual Frameworkcited by 5
  7. 2020Accelerating Reinforcement Learning Agent with EEG-based Implicit Human Feedbackcited by 4
  8. 2021DEALIO: Data-Efficient Adversarial Learning for Imitation from Observationcited by 4
  9. 2021Deception in Social Learning: A Multi-Agent Reinforcement Learning Perspectivecited by 4
  10. 2022Human-AI Shared Control via Policy Dissectioncited by 4
  11. 2022DIAMBRA Arena: a New Reinforcement Learning Platform for Research and Experimentationcited by 4
  12. 2021Cogment: Open Source Framework For Distributed Multi-actor Training, Deployment & Operationscited by 3
  13. 2022Good Time to Ask: A Learning Framework for Asking for Help in Embodied Visual Navigationcited by 3
  14. 2022A Survey on Explainable Reinforcement Learning: Concepts, Algorithms, Challengescited by 3
  15. 2021Towards Interactive Reinforcement Learning with Intrinsic Feedbackcited by 2
  16. 2022Teachable Reinforcement Learning via Advice Distillationcited by 2
  17. 2024Scalable Interactive Machine Learning for Future Command and Controlcited by 2
  18. 2025LAPP: Large Language Model Feedback for Preference-Driven Reinforcement Learningcited by 2
  19. 2021Potential Impacts of Smart Homes on Human Behavior: A Reinforcement Learning Approachcited by 1
  20. 2022Ask-AC: An Initiative Advisor-in-the-Loop Actor-Critic Frameworkcited by 1
  21. 2023Deep reinforcement learning applied to an assembly sequence planning problem with user preferencescited by 1
  22. 2024Re-Envisioning Command and Controlcited by 1
  23. 2025Automating Curriculum Learning for Reinforcement Learning using a Skill-Based Bayesian Networkcited by 1
  24. 2025Revealing Human Attention Patterns from Gameplay Analysis for Reinforcement Learningcited by 1
  25. 2026Visual Verification Enables Inference-time Steering and Autonomous Policy Improvementcited by 1
  26. 2023Socially Cognizant Robotics for a Technology Enhanced Societycited by 0
  27. 2024Prosody as a Teaching Signal for Agent Learning: Exploratory Studies and Algorithmic Implicationscited by 0
  28. 2025Actionable AI: Enabling Non Experts to Understand and Configure AI Systemscited by 0
  29. 2026Estimating Central, Peripheral, and Temporal Visual Contributions to Human Decision Making in Atari Gamescited by 0
  30. 2026Themis: An explainable AI-enabled framework for Reinforcement Learning with Human Feedbackcited by 0

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  1. 2024Unveiling the Role of Expert Guidance: A Comparative Analysis of User-centered Imitation Learning and Traditional Reinforcement Learning
  2. 2021Learning from Guided Play: A Scheduled Hierarchical Approach for Improving Exploration in Adversarial Imitation Learning
  3. 2018Hierarchical Imitation and Reinforcement Learning
  4. 2023Breadcrumbs to the Goal: Goal-Conditioned Exploration from Human-in-the-Loop Feedback
  5. 2017Deep reinforcement learning from human preferences
  6. 2022Learning from Guided Play: Improving Exploration for Adversarial Imitation Learning with Simple Auxiliary Tasks
  7. 2025Imitation Learning via Focused Satisficing
  8. 2022Improving Multimodal Interactive Agents with Reinforcement Learning from Human Feedback