A Novel Model for Arbitration between Planning and Habitual Control\n Systems
Bibliographic record
Abstract
It is well established that humans decision making and instrumental control\nuses multiple systems, some which use habitual action selection and some which\nrequire deliberate planning. Deliberate planning systems use predictions of\naction-outcomes using an internal model of the agent's environment, while\nhabitual action selection systems learn to automate by repeating previously\nrewarded actions. Habitual control is computationally efficient but may be\ninflexible in changing environments. Conversely, deliberate planning may be\ncomputationally expensive, but flexible in dynamic environments. This paper\nproposes a general architecture comprising both control paradigms by\nintroducing an arbitrator that controls which subsystem is used at any time.\nThis system is implemented for a target-reaching task with a simulated\ntwo-joint robotic arm that comprises a supervised internal model and deep\nreinforcement learning. Through permutation of target-reaching conditions, we\ndemonstrate that the proposed is capable of rapidly learning kinematics of the\nsystem without a priori knowledge, and is robust to (A) changing environmental\nreward and kinematics, and (B) occluded vision. The arbitrator model is\ncompared to exclusive deliberate planning with the internal model and exclusive\nhabitual control instances of the model. The results show how such a model can\nharness the benefits of both systems, using fast decisions in reliable\ncircumstances while optimizing performance in changing environments. In\naddition, the proposed model learns very fast. Finally, the system which\nincludes internal models is able to reach the target under the visual\nocclusion, while the pure habitual system is unable to operate sufficiently\nunder such conditions.\n
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".