Human Engagement Providing Evaluative And Informative Advice For Interactive Reinforcement Learning

Abstract

Interactive reinforcement learning proposes the use of externally-sourced information in order to speed up the learning process. When interacting with a learner agent, humans may provide either evaluative or informative advice. Prior research has focused on the effect of human-sourced advice by including real-time feedback on the interactive reinforcement learning process, specifically aiming to improve the learning speed of the agent, while minimising the time demands on the human. This work focuses on answering which of two approaches, evaluative or informative, is the preferred instructional approach for humans. Moreover, this work presents an experimental setup for a human-trial designed to compare the methods people use to deliver advice in terms of human engagement. The results obtained show that users giving informative advice to the learner agents provide more accurate advice, are willing to assist the learner agent for a longer time, and provide more advice per episode. Additi

Stats

• citations16
• S2 citations—
• github stars0
• HF likes0
• heat score9.23
• arxiv keybignold2020human

Related papers

• Reinforcement Learning With Human Advice: A Survey (2020)0.00
• Opinion-guided Reinforcement Learning (2024)0.00
• Influencing Reinforcement Learning Through Natural Language Guidance (2021)0.00
• Learning Shaping Strategies In Human-in-the-loop Interactive Reinforcement Learning (2018)0.00
• Improving Multimodal Interactive Agents With Reinforcement Learning From Human Feedback (2022)0.00
• Improving Interactive Reinforcement Learning: What Makes A Good Teacher? (2019)11.19
• Assessing Human Interaction In Virtual Reality With Continually Learning Prediction Agents Based On Reinforcement Learning Algorithms: A Pilot Study (2021)0.00
• Accelerating Reinforcement Learning Agent With Eeg-based Implicit Human Feedback (2020)0.00