Aggregation of Reinforcement Learning Algorithms
Why this work is in the frame
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Bibliographic record
Abstract
Reinforcement learning (RL) is a machine learning method that can learn an optimal strategy for a system without knowing the mathematical model of the system. Many RL algorithms are successfully applied in various fields. However, each algorithm has its advantages and disadvantages. With the increasing complexity of environments and tasks, it is difficult for a single learning algorithm to cope with complicated learning problems with high performance. This motivated us to combine some learning algorithms to improve the learning quality. This paper proposes a new multiple learning architecture, "Aggregated Multiple Reinforcement Learning System (AMRLS)". AMRLS adopts three different learning algorithms to learn individually and then combines their results with aggregation methods. To evaluate its performance, AMRLS is tested on two different environments: a Cart-pole System and a Maze environment. The presented simulation results reveal that aggregation not only provides robustness and fault tolerance ability, but also produces more smooth learning curves and needs fewer learning steps than individual learning algorithms.
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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 | 0.000 | 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 | 0.001 |
| 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