Temporal credit assignment via traces in reinforcement learning
Bibliographic record
Abstract
Reinforcement Learning is a framework for sequential decision making which is widely used in many domains such as robotics, autonomous driving, etc. Due to the sequential nature there exists the problem of assigning the credit to the actions taken in the past.This problem in reinforcement learning is known as temporal credit assignment.The problem of temporal credit assignment lies in the core of many methods such as options, online learning, off-policy learning, etc. within reinforcement learning framework.Several problems such as high variance in the value function estimates, sub-optimal policy, high sample complexity are a consequence of improper temporal credit assignment in reinforcement learning.In this thesis, we introduce and examine a couple of temporal credit assignment techniques.Specifically, we mitigate the problem of variance in value function by effectively assigning credit.First, we discuss the fundamental concepts of signals and reinforcement learning.Then, we introduce Recurrent Learning which smooths the value function along the trajectory.We then analyze the strengths of Recurrent Learning experimentally.Finally, we introduce filters from signal processing as a general framework for various traces in reinforcement learning.We show the effectiveness of filters with a couple of toy examples.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".