Waste Objects Segregation Using Deep Reinforcement Learning with Deep Q Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Effective waste classification is critical in addressing the rising environmental pollution and waste volume.Conventional sorting methods are labor-intensive and error-prone, particularly with the increasing diversity of waste materials.This study presents an innovative approach using deep reinforcement learning for waste object detection and classification to automate waste management processes.The proposed system aims to boost operational efficiency, enhance resource recovery, and reduce waste going to landfills by leveraging deep reinforcement learning.The Deep Q Network model proposed achieved an accuracy of approximately 73%.By employing DQN, an advanced reinforcement learning algorithm, the system ensures improved waste object image classification to handle complex tasks with distributional characteristics.This study can be further extended to design and develop an autonomous waste sorting system.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.006 |
| 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