Optimizing Robotic Arm Learning: Curiosity-Driven Deep Deterministic Policy Gradient
Bibliographic record
Abstract
This study explores the application of the Reinforcement Learning (RL) in training robotic arms, particularly using the Deep Deterministic Policy Gradient (DDPG) algorithm enhanced by a curiosity- driven mechanism. Robotic arms have various real-life applications, such as in the surgeries and assistive technologies. However, collecting the large- scale real-world data is costly and impractical, making simulation environments essential for optimization. The DDPG, well-suited for continuous action spaces, was employed to improve the robotic arm’s precision and adaptability. Integrating a curiosity mechanism allowed the system to explore and learn more efficiently, significantly improving the training time and success rate. The results demonstrate a 12% reduction in training time and an 18% increase in the success rate when using curiosity- driven exploration. These findings suggest that the enhanced DDPG algorithm not only accelerates learning but also enables better task execution, offering a promising approach for the real-world robotic applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".