Symbolic Plans as High-Level Instructions for Reinforcement Learning
Why this work is in the frame
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Bibliographic record
Abstract
Reinforcement learning (RL) agents seek to maximize the cumulative reward obtained when interacting with their environment. Users define tasks or goals for RL agents by designing specialized reward functions such that maximization aligns with task satisfaction. This work explores the use of high-level symbolic action models as a framework for defining final-state goal tasks and automatically producing their corresponding reward functions. We also show how automated planning can be used to synthesize high-level plans that can guide hierarchical RL (HRL) techniques towards efficiently learning adequate policies. We provide a formal characterization of taskable RL environments and describe sufficient conditions that guarantee we can satisfy various notions of optimality (e.g., minimize total cost, maximize probability of reaching the goal). In addition, we do an empirical evaluation that shows that our approach converges to near-optimal solutions faster than standard RL and HRL methods and that it provides an effective framework for transferring learned skills across multiple tasks in a given environment.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it