Integrating Neuroplasticity into Genetic Programming Agents for Adaptive Decision Making
Why this work is in the frame
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Bibliographic record
Abstract
Dynamically decomposing complex tasks into reusable subpolicies remains a core challenge in Reinforcement Learning. Tangled Program Graphs, a genetic-programming framework for general-purpose machine learning (applied here to reinforcement learning), addresses this by evolving connections between different agents in order to break down complex problems into manageable sub-problems. Inspired by memetic algorithms, which accelerate evolutionary search through agent local refinement, we introduce Neuro-Tangled Program Graphs. This biologically grounded extension utilizes hierarchical plasticity within the structure of an agent, applying a homeostatic rule at the initial decision edges and a competitive Oja-style update in each subsequent decision edge. Evaluated on both a static and dynamic variant of the MuJoCo Ant environment, this approach yields higher peak returns and evolves with 59–88% fewer mean effective instructions used per step, demonstrating stronger performance and a more compact search. These initial results suggest a promising path toward incorporating biological plausibility into memetic algorithms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it