Bibliographic record
Abstract
In this paper we pursue an approach to scaling life-long learning using parallel off-policy reinforcement learning algorithms. In life-long learning a robot continually learns from a life-time of experience, slowly acquiring and applying skills and knowledge to new situations. Many of the benefits of life-long learning are a results of scaling the amount of training data, processed by the robot, to long sensorimotor streams. Another dimension of scaling can be added by allowing off-policy sampling from the unending stream of sensorimotor data generated by a long-lived robot. Recent algorithmic developments have made it possible to apply off-policy algorithms to life-long learning, in a sound way, for the first time. We assess the scalability of these off-policy algorithms on a physical robot. We show that hundreds of accurate multi-step predictions can be learned about several policies in parallel and in realtime. We present the first online measures of off-policy learning progress. Finally we demonstrate that our robot, using the new off-policy measures, can learn 8000 predictions about 300 distinct policies, a substantial increase in scale compared to previous simulated and robotic life-long learning systems.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".