Solving POMDPs with continuous or large discrete observation spaces
Why this work is in the frame
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Bibliographic record
Abstract
We describe methods to solve partially observable Markov decision processes (POMDPs) with continuous or large discrete observation spaces. Realistic problems often have rich observation spaces, posing significant problems for standard POMDP algorithms that require explicit enumeration of the observations. This problem is usually approached by imposing an a priori discretisation on the observation space, which can be sub-optimal for the decision making task. However, since only those observations that would change the policy need to be distinguished, the decision problem itself induces a lossless partitioning of the observation space. This paper demonstrates how to find this partition while computing a policy, and how the resulting discretisation of the observation space reveals the relevant features of the application domain. The algorithms are demonstrated on a toy example and on a realistic assisted living task. 1
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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