HOBRB: Improving Task Learning With Reward Machines and Bilayer Buffers in a Hierarchical Framework
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it