An Analysis of the Effects of Hyperparameters on the Performance of Simulated Autonomous Vehicles
Bibliographic record
Abstract
Reinforcement learning (RL) is emerging as an effective technique to study autonomous vehicles (AVs) that are capable of navigating their surroundings safely and accurately. This is due to the fact that with RL, an agent can evaluate its surroundings and make appropriate decisions to maximize rewards without the need for human intervention. RL offers an alternative solution to complement Supervised learning solutions in the AV field and it offers some additional flexibility that makes it ideal to study the subject and test-focused solutions. To apply RL to AVs, we train the algorithm on a simulator, called AWS’s DeepRacer, first. The scope of this paper focuses on the hyperparameters of the algorithm and studies the performance of the model and how the hyperparameters affect it. As the need for autonomous vehicles increases to reduce traffic congestion and car crashes, it becomes significantly important to study the performance of AVs based on the hyperparameters of reinforcement learning.
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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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".