Monte Carlo off-policy reinforcement learning: a rough set approach
Bibliographic record
Abstract
This paper introduces a rough set approach to reinforcement learning by cooperating agents using a variation of the Monte Carlo off-policy control method. The problem considered in this article is how to measure the value of a state relative to the collections of similar behaviors and select an optimal policy. The solution to this problem is made possible by considering behavior patterns of swarms in the context of approximation spaces, which provide a framework for computing rough inclusion values for weights in estimating the value of a swarm state. Two different forms of the Monte Carlo off-policy reinforcement learning method are considered as a part of a study of learning in real-time by a swarm. The contribution of this article is the presentation of a new Monte Carlo off-policy control method defined in the context of approximation spaces. The ecosystem provided by swarms of zebra danio fish (Brachydanio Rerio) has been selected to facilitate study of reinforcement learning. This ecosystem is briefly described. In addition, the results of experiments using reinforcement learning techniques to simulate swarm behavior of the zebra danio ecosystem for two forms of the Monte Carlo off-policy control method are given.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".