PADDLE: Logic Program Guided Policy Reuse in Deep Reinforcement Learning
Bibliographic record
Abstract
Learning new skills through previous experience is regular in human life, which is the core idea of Transfer Reinforcement Learning (TRL). TRL requires the agent to learn when and which source policy is the best to reuse as the target task's policy and how to reuse the source policy. Most TRL methods learn, transfer, and reuse black-box policies, which is hard to explain: 1) when to reuse, 2) which source policy is effective, and reduces transfer efficiency. In this paper, we propose a novel TRL method called ProgrAm gui DeD poLicy rEuse (PADDLE). PADDLE can measure the logic similarities between tasks and transfer knowledge which reflects the logic behind the target task. To achieve this, we propose a hybrid decision model that synthesizes high-level logic programs and learns low-level DRL policy to learn source tasks. Second, we propose a transferability metric that can measure the logic similarity between the target task and source tasks. Last, we combine it with the low-level policy similarity to select the appropriate source policy as the guiding policy for the target task. Experimental results show that PADDLE can effectively select the appropriate source tasks to guide learning on the target task, outperforming black-box TRL methods.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".