[2207.09845] Quantifying the Effect of Feedback Frequency in Interactive Reinforcement Learning for Robotic Tasks Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Robotics arXiv:2207.09845 (cs) [Submitted on 20 Jul 2022 ( v1 ), last revised 15 Mar 2023 (this version, v2)] Title: Quantifying the Effect of Feedback Frequency in Interactive Reinforcement Learning for Robotic Tasks Authors: Daniel Harnack , Julie Pivin-Bachler , Nicolás Navarro-Guerrero View a PDF of the paper titled Quantifying the Effect of Feedback Frequency in Interactive Reinforcement Learning for Robotic Tasks, by Daniel Harnack and Julie Pivin-Bachler and Nicol\'as Navarro-Guerrero View PDF HTML (experimental) Abstract: Reinforcement learning (RL) has become widely adopted in robot control. Despite many successes, one major persisting problem can be very low data efficiency. One solution is interactive feedback, which has been shown to speed up RL considerably. As a result, there is an abundance of different strategies, which are, however, primarily tested on discrete grid-world and small scale optimal control scenarios. In the literature, there is no consensus about which feedback frequency is optimal or at which time the feedback is most beneficial. To resolve these discrepancies we isolate and quantify the effect of feedback frequency in robotic tasks with continuous state and action spaces. The experiments encompass inverse kinematics learning for robotic manipulator arms of different complexity. We show that seemingly contradictory reported phenomena occur at different complexity levels. Furthermore, our results suggest that no single ideal feedback frequency exists. Rather that feedback frequency should be changed as the agent's proficiency in the task increases. Comments: Neural Computing and Applications (2022). Special Issue on Human-aligned Reinforcement Learning for Autonomous Agents and Robots Subjects: Robotics (cs.RO) ; Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG) Cite as: arXiv:2207.09845 [cs.RO] (or arXiv:2207.09845v2 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2207.09845 Focus to learn more arXiv-issued DOI via DataCite Related DOI : https://doi.org/10.1007/s00521-022-07949-0 Focus to learn more DOI(s) linking to related resources Submission history From: Nicolás Navarro-Guerrero [ view email ] [v1] Wed, 20 Jul 2022 12:17:02 UTC (979 KB) [v2] Wed, 15 Mar 2023 16:06:29 UTC (991 KB) Full-text links: Access Paper: View a PDF of the paper titled Quantifying the Effect of Feedback Frequency in Interactive Reinforcement Learning for Robotic Tasks, by Daniel Harnack and Julie Pivin-Bachler and Nicol\'as Navarro-Guerrero View PDF HTML (experimental) TeX Source view license Current browse context: cs.RO < prev | next > new | recent | 2022-07 Change to browse by: cs cs.AI cs.HC cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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