Active Roll-outs in MDP with Irreversible Dynamics
Bibliographic record
Abstract
In Reinforcement Learning (RL), regret guarantees scaling with the square root of the time horizon have been shown to hold only for communicating Markov decision processes (MDPs) where any two states are connected. This essentially means that an algorithm can eventually recover from any mistake. However, real-world tasks usually include situations where taking a single "bad" action can permanently trap a learner in a suboptimal region of the state-space. Since it is provably impossible to achieve sub-linear regret in general multi-chain MDPs, we assume a weak mechanism that allows the learner to request additional information. Our main contribution is to address: (i) how much external information is needed, (ii) how and when to use it, and (iii) how much regret is incurred. We design an algorithm that minimizes requests for external information in the form of rollouts of a policy specified by the learner by actively requesting it only when needed. The algorithm provably achieves O(√ T) active regret after T steps in a large class of multi-chain MDPs, by only requesting O(log(T)) rollout transitions. The superiority of our algorithm to standard algorithms such as R-Max and UCRL is demonstrated in experiments on some illustrative grid-world examples. (a) (b) (c) Figure 1: Example of (a) a communicating MDP, (b) a unichain MDP with a single recurrent class, and (c) a multi-chain MDP with two recurrent classes. The circles represent states while the labeled edges represent transitions due to executing actions {a, b, c}.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".