1110 Reinforcement Learning in Consideration of Symmetry
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
This work presents a new approach to reinforcement learning in consideration of symmetries. At first we formalize the concept of symmetry in Markov decision processes (MDPs) and derive theoretical results using this formalism. The second we made a check on these theorem by the simulation. Reinforcement Learning is one of the fields studied briskly recently. But in order to use it in the real world, there are some problems. One of them is associated with very long calculation time and amount of calculation. Then, in order to solve them symmetry is used in this research. At first, the agent check condition of the map. And if the map is symmetry, we use our theorem that is reignforcement of consideration symmetries. When this result is seen, it turns out that it is being early completed by the way of the result in consideration of symmetry.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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