Automatic Policy Decomposition through Abstract State Space Dynamic Specialization
Bibliographic record
Abstract
Significant progress has been made recently in deep reinforcement learning in the development of options. This idea consists in learning policies (or macro of actions) for sub-goals. An important bottleneck of this approach is that these options are often available as actions everywhere in the state space, hence, potentially enlarging the action space to search for the optimal policy. In this paper, we propose to use the fact that the state space is rarely fully connected, but instead has regions of highly connected states with fewer links between those regions. Our proposed model extends deep Q-Learning network (DQN) by splitting the top layers into multiple heads each specializing in learning the dynamics of a particular region of the state space as well as the optimal policy for that region. The state prediction quality of each head is used to determine which head is the local expert, rating its contribution to the current state's policy. We show that this approach is able to learn something similar to options and generalized value function, providing a promising alternative to the current approach.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".