Bibliographic record
Abstract
Imitation learning is a supervised learning problem that involves training a model to perform a task in a given environment using demonstrations of an expert. In this thesis, we propose 5 metrics to evaluate the performance of imitation learning agents. We compare state-of-the-art imitation learning models to deep neural networks at imitating state-based and reactive behavior. To compare the imitation learning techniques, we use two partially observable domains: the continuous RoboCup domain and the discrete Vacuum Cleaner domain. We show how our proposed metrics provide us with more qualitative information about the performance of imitation learners when imitating state-based behavior compared to state-of-the-art metrics. In addition, we show how our testing methodology provides results that resemble the eye-test that current testing methodologies fail to provide. We also show how Long Short-Term Memory (LSTM) networks outperform state-of-the-art models at imitating state-based behavior in the RoboCup soccer domain. i
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".