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Integrating Human Expertise in Continuous Spaces: A Novel Interactive Bayesian Optimization Framework with Preference Expected Improvement

arXiv 2401.12662 ↗First posted 23 Jan 2024cs.RO cs.AI cs.HCcited by 0

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Abstract (arXiv)

Interactive Machine Learning (IML) seeks to integrate human expertise into machine learning processes. However, most existing algorithms cannot be applied to Realworld Scenarios because their state spaces and/or action spaces are limited to discrete values. Furthermore, the interaction of all existing methods is restricted to deciding between multiple proposals. We therefore propose a novel framework based on Bayesian Optimization (BO). Interactive Bayesian Optimization (IBO) enables collaboration between machine learning algorithms and humans. This framework captures user preferences and provides an interface for users to shape the strategy by hand. Additionally, we've incorporated a new acquisition function, Preference Expected Improvement (PEI), to refine the system's efficiency using a probabilistic model of the user preferences. Our approach is geared towards ensuring that machines can benefit from human expertise, aiming for a more aligned and effective learning process. In the course of this work, we applied our method to simulations and in a real world task using a Franka Panda robot to show human-robot collaboration.

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References · 16

  1. 2017Deep reinforcement learning from human preferencescited by 3,912
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  4. 2018A Tutorial on Bayesian Optimizationcited by 744
  5. 2021PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-trainingcited by 302
  6. 2017Interactive Learning from Policy-Dependent Human Feedbackcited by 158
  7. 2017Deep TAMER: Interactive Agent Shaping in High-Dimensional State Spacescited by 123
  8. 2022Few-Shot Preference Learning for Human-in-the-Loop RLcited by 87
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  10. 2022Preference Exploration for Efficient Bayesian Optimization with Multiple Outcomescited by 42
  11. 2018DQN-TAMER: Human-in-the-Loop Reinforcement Learning with Intractable Feedbackcited by 41
  12. 2021A Survey on Interactive Reinforcement Learning: Design Principles and Open Challengescited by 41
4 more
  1. 2023Efficient Preference-Based Reinforcement Learning Using Learned Dynamics Modelscited by 21
  2. 2018Interactive Learning with Corrective Feedback for Policies based on Deep Neural Networkscited by 10
  3. 2022Interactive Machine Learning: A State of the Art Reviewcited by 6
  4. 2021On Preference Learning Based on Sequential Bayesian Optimization with Pairwise Comparisoncited by 3

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