Bibliographic record
Abstract
The overestimation of action values caused by randomness in rewards can harm the ability to learn and the performance of reinforcement learning agents. This maximization bias has been well established and studied in the off-policy Q-learning algorithm. However, less study has been done for on-policy algorithms such as Sarsa and its variants. We conduct a thorough empirical analysis on Sarsa, Expected Sarsa, and n-step Sarsa. We find that the on-policy Sarsa variants suffer from less maximization bias than off-policy Q-learning in several test environments. We show how the choice of hyper-parameters impacts the severity of the bias. A decaying learning rate schedule results in more maximization bias than a fixed learning rate. Larger learning rates lead to larger overestimation. A larger exploration parameter leads to worse bias in Q-learning but less bias in the on-policy algorithms. We also show that a larger variance in rewards leads to more bias in both Q-Learning and Sarsa., but Sarsa is less affected than Q-learning.
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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 | 0.012 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 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".