The Online Loop-free Stochastic Shortest-Path Problem.
Bibliographic record
Abstract
We consider a stochastic extension of the loop-free shortest path problem with adversarial rewards. In this episodicMarkov decision problem an agent traverses through an acyclic graph with random transitions: at each step of an episode the agent chooses an action, receives some reward, and arrives at a random next state, where the reward and the distribution of the next state depend on the actual state and the chosen action. We consider the bandit situation when only the reward of the just visited state-action pair is revealed to the agent. For this problem we develop algorithms that perform asymptotically as well as the best stationary policy in hindsight. Assuming that all states are reachable with probability a>0 under all policies, we give an algorithm and prove that its regret is O(L^2 sqrt(T|A|)/a), where T is the number of episodes, A denotes the (finite) set of actions, and L is the length of the longest path in the graph. Variants of the algorithm are given that improve the dependence on the transition probabilities under specific conditions. The results are also extended to variations of the problem, including the case when the agent competes with time varying policies.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".