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 (RL) is a learning technique that provides a means for learning an optimal control policy when the dynamics of the environment under consideration is unavailable [L.P. Kaelbling et al., 1996, R.S. Sutton and A.G. Barto, 1998]. While RL has been successfully applied in many single or multiple agents systems [S. Arai et al., 2000, H.R. Berenji and D.A. Vengerov, 2000, M. Tan, 1993, Y. Nagayuki et al., 2000], the learning quality is greatly influenced by learning algorithms and their parameters. Setting of the parameters of RL algorithms is something of a black art, and small differences in these parameters can lead to large differences in learning qualities. Determining the best algorithm and the optimal parameters can be costly in terms of time and computation. Even if the cost is acceptable, the robustness of learning is still a question. In order to address the difficulty, an aggregated multiagent reinforcement learning system (AMRLS) is proposed to deal with the RL environment as a multiagent environment. A maze world environment is used to validate the AMRLS. Experimental results illustrate that compared with normal Q(/spl lambda/)-learning and SARSA(/spl lambda/) algorithms, the AMRLS increases both the learning speed and the rate of reaching the shortest path.
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 | 0.000 | 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 | 0.000 | 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