Continual Reinforcement Learning for Autonomous Driving with Application on Velocity Control under Various Environment
Bibliographic record
Abstract
Reinforcement learning based methods are extensively studied in autonomous driving. However, most existing methods, suffering from catastrophic forgetting, only work properly under certain scenarios. Therefore, this paper proposes a continual reinforcement learning approach to improve the control adaptability of autonomous driving systems. Specifically, the framework is based on a Soft Actor-Critic algorithm, which includes a shared feature extractor with regularization loss to learn task-specific output mapping. A linear multi-policy heads structure is designed to continuously learn different tasks without interference. Velocity control under various environment is taken as a case study, and three velocity control tasks with a high degree of overlap in the observation spaces are designed. In addition, multi-modal optimization objectives considering safety, efficiency and comfort for the three tasks are designed to evaluate the effectiveness of the proposed approach. The simulation studies are conducted in CARLA, which demonstrates the efficiency of the proposed method in terms of improving the continual learning capability of the model. Overall, the proposed continual reinforcement learning framework contributes to the development of adaptive autonomous driving systems to a large extent.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".