Tradeoff between exploration and exploitation of OQ(λ) with non-Markovian update in dynamic environments
Bibliographic record
Abstract
This paper presents some investigations on tradeoff between exploration and exploitation of opposition-based Q(lambda) with non-Markovian update (NOQ(lambda)) in a dynamic environment. In the previous work the authors applied NOQ(lambda) to the deterministic GridWorld problem. In this paper, we have implemented the NOQ(lambda) algorithm for a simple elevator control problem to test the behavior of the algorithm for non-deterministic and dynamic environment. We also extend the NOQ(lambda) algorithm by introducing the opposition weight to find a better tradeoff between exploration and exploitation for the NOQ(lambda) technique. The value of the opposition weight increases as the number of steps increases. Hence, it has more positive effects on the Q-value updates for opposite actions as the learning progresses. The performance of NOQ(lambda) method is compared with Q(lambda) technique. The experiments indicate that NOQ(lambda) performs better than Q(lambda).
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".