Continuous and discretized pursuit learning schemes: various algorithms and their comparison
Bibliographic record
Abstract
A learning automaton (LA) is an automaton that interacts with a random environment, having as its goal the task of learning the optimal action based on its acquired experience. Many learning automata (LAs) have been proposed, with the class of estimator algorithms being among the fastest ones, Thathachar and Sastry, through the pursuit algorithm, introduced the concept of learning algorithms that pursue the current optimal action, following a reward-penalty learning philosophy. Later, Oommen and Lanctot extended the pursuit algorithm into the discretized world by presenting the discretized pursuit algorithm, based on a reward-inaction learning philosophy. In this paper we argue that the reward-penalty and reward-inaction learning paradigms in conjunction with the continuous and discrete models of computation, lead to four versions of pursuit learning automata. We contend that a scheme that merges the pursuit concept with the most recent response of the environment, permits the algorithm to utilize the LAs long-term and short-term perspectives of the environment. In this paper, we present all four resultant pursuit algorithms, prove the E-optimality of the newly introduced algorithms, and present a quantitative comparison between them.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".