A New Online Evolutive Optimization Method for Driving Agents
Bibliographic record
Abstract
As one of the research objects in machine learning, agents are endowed with the ability to perceive and make decisions. In order to meet the needs of different applications, many researchers focus on algorithms for controlling agents. In most current paradigms, the control algorithms take the information of the surrounding environment as input and output actions. However, under the influence of this unidirectional data flow, the upper limit of the performance of control algorithms depends on the accuracy and fit measure of the environment information. Even though recent end-to-end control algorithms take the environmental raw data as input and reduce the influence of perception performance on it, the raw data acquisition method is still fixed, therefore, the performance of these control algorithms is still limited by the environmental information acquisition scheme. Meanwhile, although most control methods have the ability to update parameters online, they usually do not have the ability to optimize the acquisition of environmental information, and there may be unknown situations that are not in the data set used for pre-training. As a result, current methods cannot handle detection inaccuracies and unknown situations, and lack the ability to further improve their own performance online. In order to resolve the deficiency of the traditional control paradigms, we introduce the reverse optimization channel from the controller to the environmental information acquisition scheme to form a new algorithm through communication between devices. Experiments show that our method has significant performance margin and universal online evolutive learning ability, as compared to traditional paradigms.
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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 | 0.000 | 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 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".