A novel hybrid learning technique applied to a self-learning multi-robot system
Bibliographic record
Abstract
This paper mainly discusses learning in pursuit-evasion game. In the pursuit-evasion model, one robot pursues another one in a partially known environment. Partially known environment means that each robot knows the instant position of the other robot but at the same time none of them knows its control strategy. Therefore, both robots have to self-learn their control strategies on-line by interaction with each other. A new hybrid learning technique is proposed. The proposed technique combines reinforcement learning with both a fuzzy controller and genetic algorithms in a two-phase structure. The proposed technique is called a Q(¿)-learning based genetic fuzzy controller (QLBGFC). To test the performance of our proposed technique, it is compared with the optimal strategy, the Q(¿)-learning, and the reward-based genetic algorithms. Computer simulations show the usefulness of the proposed technique. In addition, the convergence and the boundedness of the Q-learning algorithm used in the proposed technique are shown.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".