Meta-learning for Predictive Knowledge Architectures: A Case Study Using TIDBD on a Sensor-rich Robotic Arm
Bibliographic record
Abstract
Predictive approaches to modelling the environment have seen recent successes in robotics and other long-lived applications. These predictive knowledge architectures are learned incrementally and online, through interaction with the environment. One challenge for applications of predictive knowledge is the necessity of tuning feature representations and parameter values: no single step size will be appropriate for every prediction. Furthermore, as sensor signals might be subject to change in a non-stationary world, predefined step sizes cannot be sufficient for an autonomous agent. In this paper, we explore Temporal-Difference Incremental Delta-Bar-Delta (TIDBD)-a meta-learning method for temporal-difference (TD) learning which adapts a vector of many step sizes, allowing for simultaneous step size tuning and representation learning. We demonstrate that, for a predictive knowledge application, TIDBD is a viable alternative to tuning step-size parameters, by showing that the performance of TIDBD is comparable to that of TD with an exhaustive parameter search. Performance here is measured in terms of root mean squared difference from the true value, calculated offline. Moreover, TIDBD can perform representation learning, potentially supporting robust learning in the face of failing sensors. The ability for an autonomous agent to adapt its own learning and adjust its representation based on interactions with its environment is a key capability. With its potential to fulfill these desiderata, meta-learning is a promising component for future systems.
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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 | 0.000 | 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 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".