Prediction in Intelligence: An Empirical Comparison of Off-policy Algorithms on Robots
Bibliographic record
Abstract
The ability to continually make predictions about the world may be central to intelligence. Off-policy learning and general value functions (GVFs) are well-established algorithmic techniques for learning about many signals while interacting with the world. In the past couple of years, many ambitious works have used off-policy GVF learning to improve control performance in both simulation and robotic control tasks. Many of these works use semi-gradient temporal-difference (TD) learning algorithms, like Q-learning, which are potentially divergent. In the last decade, several TD learning algorithms have been proposed that are convergent and computationally efficient, but not much is known about how they perform in practice, especially on robots. In this work, we perform an empirical comparison of modern off-policy GVF learning algorithms on three different robot platforms, providing insights into their strengths and weaknesses. We also discuss the challenges of conducting fair comparative studies of off-policy learning on robots and develop a new evaluation methodology that is successful and applicable to a relatively complicated robot domain.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".