Broad-persistent Advice for Interactive Reinforcement Learning Scenarios
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Abstract (arXiv)
The use of interactive advice in reinforcement learning scenarios allows for speeding up the learning process for autonomous agents. Current interactive reinforcement learning research has been limited to real-time interactions that offer relevant user advice to the current state only. Moreover, the information provided by each interaction is not retained and instead discarded by the agent after a single use. In this paper, we present a method for retaining and reusing provided knowledge, allowing trainers to give general advice relevant to more than just the current state. Results obtained show that the use of broad-persistent advice substantially improves the performance of the agent while reducing the number of interactions required for the trainer.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 6 of its 23 references that are in the map. First posted on arXiv 11 Oct 2022.
Builds on
6 of 23 references.
References · 6
- 2021How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learnedcited by 201
- 2021A Survey on Interactive Reinforcement Learning: Design Principles and Open Challengescited by 41
- 2020A Conceptual Framework for Externally-influenced Agents: An Assisted Reinforcement Learning Reviewcited by 15
- 2020A Comparison of Humanoid Robot Simulators: A Quantitative Approachcited by 8
- 2020Human Engagement Providing Evaluative and Informative Advice for Interactive Reinforcement Learningcited by 7
- 2021Persistent Rule-based Interactive Reinforcement Learningcited by 5
Built on by
Citing papers per year: 2024: 1.
1 paper.