An Inverse Reinforcement Learning Algorithm for Partially Observable Domains with Application on Healthcare Dialogue Management
Bibliographic record
Abstract
In this paper, we propose an algorithm for learning a reward model from an expert policy in partially observable Markov decision processes (POMDPs). The problem is formulated as inverse reinforcement learning (IRL) in the POMDP framework. The proposed algorithm then uses the expert trajectories to find an unknown reward model-based on the known POMDP model components. Similar to previous IRL work in Markov Decision Processes (MDPs), our algorithm maximizes the sum of the margin between the expert policy and the intermediate candidate policies. However, in contrast to previous work, the expert and intermediate candidate policy values are approximated using the beliefs recovered from the expert trajectories, specifically by approximating expert belief transitions. We apply our IRL algorithm to a healthcare dialogue POMDP where the POMDP model components are estimated from real dialogues. Our experimental results show that the proposed algorithm is able to learn a reward model that accounts for the expert policy.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".