A two stage learning technique for dual learning in the pursuit-evasion differential game
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Bibliographic record
Abstract
This paper addresses the case of dual learning in the pursuit-evasion (PE) differential game and examines how fast the players can learn their default control strategies. The players should learn their default control strategies simultaneously by interacting with each other. Each player's learning process depends on the rewards received from its environment. The learning process is implemented using a two stage learning algorithm that combines the particle swarm optimization (PSO)-based fuzzy logic control (FLC) algorithm with the Q-Learning fuzzy inference system (QFIS) algorithm. The PSO algorithm is used as a global optimizer to autonomously tune the parameters of a fuzzy logic controller whereas the QFIS algorithm is used as a local optimizer. The two stage learning algorithm is compared through simulation with the default control strategy, the PSO-based FLC algorithm, and the QFIS algorithm. Simulation results show that the players are able to learn their default control strategies. Also, it shows that the two stage learning algorithm outperforms the PSO-based FLC algorithm and the QFIS algorithm with respect to the learning time.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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