Deep Reinforcement Learning Based Coverage Path Planning in Unknown Environments
Why this work is in the frame
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Bibliographic record
Abstract
The Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm offers a robust solution for the coverage path planning problem, where a robot must effectively and efficiently cover a designated area, ensuring minimal redundancy and maximum coverage. Traditional methods for path planning often lack the adaptability required for dynamic and unstructured environments. In contrast, TD3 utilizes twin Q-networks to reduce overestimation bias, delayed policy updates for increased stability, and target policy smoothing to maintain smooth transitions in the robot's path. These features allow the robot to learn an optimal path strategy in real-time, effectively balancing exploration and exploitation. This paper explores the application of TD3 to coverage path planning, demonstrating that it enables a robot to adaptively and efficiently navigate complex coverage tasks, showing significant advantages over conventional methods in terms of coverage rate, total length, and adaptability.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it