Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Reinforcement learning is a machine intelligence scheme for learning in highly dynamic, probabilistic environments. By interaction with the environment, reinforcement agents learn optimal control policies, especially in the absence of a priori knowledge and/or a sufficiently large amount of training data. Despite its advantages, however, reinforcement learning suffers from a major drawback - high calculation cost because convergence to an optimal solution usually requires that all states be visited frequently to ensure that policy is reliable. This is not always possible, however, due to the complex, high-dimensional state space in many applications. This paper introduces opposition-based reinforcement learning, inspired by opposition-based learning, to speed up convergence. Considering opposite actions simultaneously enables individual states to be updated more than once shortening exploration and expediting convergence. Three versions of Q-learning algorithm will be given as examples. Experimental results for the grid world problem of different sizes demonstrate the superior performance of the proposed approach.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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