HOBRB: Improving Task Learning With Reward Machines and Bilayer Buffers in a Hierarchical Framework
Bibliographic record
Abstract
Despite significant advancements in both theory and practical applications, such as neural architecture search and hyperparameter optimization, deep reinforcement learning still faces a variety of challenges. Two particularly pressing concerns are the inefficient use of samples and the difficulty in crafting effective reward functions. To address these challenges, we propose a novel hierarchical reinforcement learning (HRL) framework. The innovation of our approach lies in the design of two mechanisms: a segmented reward mechanism and a multi-level experience buffer mechanism. The segmented reward mechanism facilitates the agent's comprehension of the underlying structure of the reward function, fostering a deeper grasp of the task's essence. The multi-level experience buffer mechanism includes bilayer replay buffers, with a core buffer for general experiences and a branch buffer for subtask-related experiences. These mechanisms accelerate policy learning and enhance the agent's task completion capabilities. Experimental evaluations were conducted on single-task and multitask tests across various environments, demonstrating significant performance improvements compared to baseline algorithms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".