Empowering Generalization for Deep Reinforcement Learning via Symbolic Planning
Bibliographic record
Abstract
Deep Reinforcement Learning (RL) has achieved notable success across various domains. However, it still faces the sample-inefficiency problem that requires massive training samples to learn the optimal policy. Furthermore, the trained policy highly depends on the training environment, limiting the generalization, especially in long-horizon tasks. In this paper, we propose Plan-guided Exploration and generAlization for Reinforcement Learning (PEARL) to explore how symbolic planning can help Deep RL in terms of efficiency and generalization. PEARL is a two-level structure that incorporates any symbolic planner as the meta-controller to guide the low-level RL agent in finishing the long-horizon tasks. Furthermore, we automatically learn the mapping between the two levels based on a small dataset to relax the assumption of human efforts. We evaluate PEARL on Montezuma's Revenge and show that PEARL outperforms previous hierarchical and symbolic-planning-based methods in terms of efficiency and generalization.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".