Context-switching and adaptation: Brain-inspired mechanisms for handling environmental changes
Bibliographic record
Abstract
Reinforcement learning (RL) allows an intelligent agent to learn optimal behavior as it interacts with its environment. Conventional model-based RL algorithms learn rapidly, but can be slow to adapt to sudden changes in the environment. Animals' brains, however, are thought to employ model-based RL mechanisms for learning, but are able to adapt to changes with relative ease. By employing “transfer learning”, they can recycle previously learned information to solve new problems with minimal new learning. We developed two brain-inspired methods that can allow model-based RL to cope with changes to the underlying process being learned: hierarchical state abstraction, and context-switching. Hierarchical state abstraction allows a previously-learned model to be efficiently adapted for use in a new task, while context switching allows learned models to be saved and recalled at the appropriate times. We test these mechanisms using grid-world simulations in which the goal remains constant, but contingencies for reaching it frequently change. These mechanisms allow an agent to significantly outperform a conventional model-based RL algorithm in the task.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".