Download Report IEEE DataPort : CQ(λ) Human-in-the-Loop Reinforcement Learning Dataset for Robotic Bag-Shaking Control - 2026
CSV by Uri Kartoun
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Format: CSV Publisher: IEEE DataPort Publication Date of the Electronic Edition: 02/24/2026
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ISBN: 10.21227/dpjw-w997
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Description
This dataset supports comparative research on autonomous and human-guided reinforcement learning for robotic manipulation. It extends prior work on human–robot collaborative learning (Kartoun, Stern, & Edan, IEEE SMC, 2006; Journal of Intelligent & Robotic Systems, 2010) by providing structured, reproducible experimental data for modern policy evaluation and adaptive control analysis.The dataset accompanies a simulation study of CQ(λ) — Cooperative Q-learning with eligibility traces — applied to a bag-shaking task in which a robotic agent must extract knotted objects under partially observable conditions (hidden knot tightness and bag entanglement). It comprises 10,000 episodes across 10 independent runs and two learning conditions: standard Q(λ) and CQ(λ), in which a human expert provides linguistic corrective guidance ("significantly increase", "slightly decrease", etc.) whenever rolling task performance drops below a defined threshold. Approximately 500,000 step-level state–action transitions and 1,200 intervention records are included, alongside episode-level reward trajectories, success flags, exploration schedules, and timing data.CQ(λ) achieves roughly 30% higher accumulated reward, a 21 percentage-point improvement in success rate, and 14% faster episode completion relative to autonomous Q(λ), demonstrating the practical value of performance-triggered linguistic policy shaping. The data are intended to support research in human-in-the-loop reinforcement learning, sample efficiency analysis, intervention strategy design, and benchmarking of policy shaping methods within collaborative robotic frameworks.
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Delivery time: Instant