Preferential Proximal Policy Optimization
Bibliographic record
Abstract
The Proximal Policy Optimization (PPO) is a policy gradient approach providing state-of-the-art performance in many domains through the “surrogate” objective function using stochastic gradient ascent. While PPO is an appealing approach in reinforcement learning, it does not consider the importance of states (a frequently seen state in a successful trajectory) in policy/value function updates. In this work, we introduce Preferential Proximal Policy Optimization (P3O) which incorporates the importance of these states into parameter updates. First, we determine the importance of each state based on the variance of the action probabilities given a particular state multiplied by the value function, normalized and smoothed using the Exponentially Weighted Moving Average. Then, we incorporate the state's importance in the surrogate objective function. That is, we redefine value and advantage estimation objectives functions in the PPO approach. Unlike other related approaches, we select the importance of states automatically which can be used for any algorithm utilizing a value function. Empirical evaluations across six Atari environments demonstrate that our approach significantly outperforms the baseline (vanilla PPO) across different tested environments, highlighting the value of our proposed method in learning complex environments.
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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 | 0.007 |
| 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 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".