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 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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".