The residual gradient FACL algorithm for differential games
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Bibliographic record
Abstract
A new fuzzy reinforcement learning algorithm that tunes the input and the output parameters of a fuzzy logic controller is proposed in this paper. The proposed algorithm uses three fuzzy inference systems (FISs); one is used as an actor (fuzzy logic controller, FLC), and the other two FISs are used as critics. The proposed algorithm uses the residual gradient value iteration algorithm described in [4] to tune the input and the output parameters of the actor (FLC) of the learning robot. The proposed algorithm also tunes the input and the output parameters of the critics. The proposed algorithm is called the residual gradient fuzzy actor critics learning (RGFACL) algorithm. The proposed algorithm is used to learn a single pursuit-evasion differential game. Simulation results show that the performance of the proposed RGFACL algorithm outperforms the performance of the fuzzy actor critic learning (FACL) and the Q-learning fuzzy inference system (QLFIS) algorithms proposed in [3] and [7], respectively, in terms of convergence and speed of learning.
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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 | 0.000 | 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 | 0.001 | 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 it