Greedy linear value-approximation for factored Markov decision processes
Bibliographic record
Abstract
Significant recent work has focused on using linear representations to approximate value functions for factored Markov decision processes (MDPs). Current research has adopted linear programming as an effective means to calculate approximations for a given set of basis functions, tackling very large MDPs as a result. However, a number of issues remain unresolved: How accurate are the approximations produced by linear programs? How hard is it to produce better approximations? and Where do the basis functions come from? To address these questions, we first investigate the complexity of minimizing the Bellman error of a linear value function approximation—showing that this is an inherently hard problem. Nevertheless, we provide a branch and bound method for calculating Bellman error and performing approximate policy iteration for general factored MDPs. These methods are more accurate than linear programming, but more expensive. We then consider linear programming itself and investigate methods for automatically constructing sets of basis functions that allow this approach to produce good approximations. The techniques we develop are guaranteed to reduce error, but can also empirically reduce Bellman error. 1
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".