Model-based reinforcement learning with nearly tight exploration complexity bounds
Why this work is in the frame
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Bibliographic record
Abstract
One might believe that model-based algorithms of reinforcement learning can propagate the obtained experience more quickly, and are able to direct exploration better. As a consequence, fewer exploratory actions should be enough to learn a good policy. Strangely enough, current theoretical results for model-based algorithms do not support this claim: In a finite Markov decision process with N states, the best bounds on the number of exploratory steps necessary are of order O(N 2 log N), in contrast to the O(N log N) bound available for the modelfree, delayed Q-learning algorithm. In this paper we show that Mormax, a modified version of the Rmax algorithm needs to make at most O(N log N) exploratory steps. This matches the lower bound up to logarithmic factors, as well as the upper bound of the state-of-the-art model-free algorithm, while our new bound improves the dependence on other problem parameters. In the reinforcement learning (RL) framework, an agent interacts with an unknown environment and tries to maximize its long-term profit. A standard way to measure the efficiency of the agent is sample complexity or exploration complexity. Roughly, this quantity tells how many non-optimal (exploratory) steps does the agent make at most. The best understood and most studied case is when the environment is a finite Markov decision process (MDP) with the expected total discounted reward criterion. Since the work of Kearns & Singh (1998), many algorithms have been published with bounds on their sam-
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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