Modeling long-term nutritional behaviors using deep homeostatic reinforcement learning
Bibliographic record
Abstract
The continual generation of behaviors that satisfy all conflicting demands that cannot be satisfied simultaneously, is a situation that is seen naturally in autonomous agents such as long-term operating household robots, and in animals in the natural world. Homeostatic reinforcement learning (homeostatic RL) is known as a bio-inspired framework that achieves such multiobjective control through behavioral optimization. Homeostatic RL achieves autonomous behavior optimization using only internal body information in complex environmental systems, including continuous motor control. However, it is still unknown whether the resulting behaviors actually have the similar long-term properties as real animals. To clarify this issue, this study focuses on the balancing of multiple nutrients in animal foraging as a situation in which such multiobjective control is achieved in animals in the natural world. We then focus on the nutritional geometry framework, which can quantitatively handle the long-term characteristics of foraging strategies for multiple nutrients in nutritional biology, and construct a similar verification environment to show experimentally that homeostatic RL agents exhibit long-term foraging characteristics seen in animals in nature. Furthermore, numerical simulation results show that the long-term foraging characteristics of the agent can be controlled by changing the weighting for the agent's multiobjective motivation. These results show that the long-term behavioral characteristics of homeostatic RL agents that perform behavioral emergence at the motor control level can be predicted and designed based on the internal dynamics of the body and the weighting of motivation, which change in real time.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".