Model-Based Reinforcement Learning With Probabilistic Ensemble Terminal Critics for Data-Efficient Control Applications
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 article proposes a data-efficient model-based reinforcement learning (RL) algorithm empowered by reliable future reward estimates achieved through a confidence-based probabilistic ensemble terminal critics (PETC). The proposed algorithm utilizes a model-predictive controller to choose an action that optimizes the sum of the near and distant future rewards for a given current state. Near future rewards with high confidence are determined directly from trained deterministic dynamics and reward models. Distant future rewards beyond these horizons are meticulously assessed using the proposed confidence-based PETC, which minimizes estimation errors inherent in the distant future and quantifies uncertainty confidence. Through such confidence-based guided actions, the proposed approach is expected to operate in a reliable, explainable, and data-efficient manner, consistently guiding the system to an optimal trajectory. A comparison with the existing state-of-the-art RL algorithms for eight DeepMind Control Suite tasks confirms the superior data efficiency of the proposed approach, which achieves an average cumulative reward of 761.2 in merely 500K steps, whereas the other algorithms score below 700.0. The proposed algorithm is also successfully applied to two real-world control applications, namely single- and double-cartpole swing-up tasks.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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