Visual transfer for reinforcement learning via gradient penalty based Wasserstein domain confusion
Bibliographic record
Abstract
It is pretty challenging to transfer learned policies among different visual environments. The recently proposed Wasserstein Adversarial Proximal Policy Optimization (WAPPO) attempts to overcome this difficulty by definitely learning a representation, which is sufficient to express the originating and the target domains simultaneously. Specifically, WAPPO uses the Wasserstein Confusion target function to force reinforcement learning (RL) agents to learn the mapping from visually different environments to domain-independent expressions, thereby achieving better domain adaptation performance in RL. However, WAPPO uses weight clipping to strengthen the Lipschitz continuity of the Wasserstein Confusion target function, which results in poor manifestation. In this paper, we present Gradient Penalty based Wasserstein Adversarial Proximal Policy Optimization (GPWAPPO), a new approach for the visual transfer in RL that learns to match the distributions of distilled characteristics between an originating domain and the objective domain. Specifically, we propose a new target function, Gradient Penalty-based Wasserstein Confusion (GPWC), which uses selective clipping weights to catch up with the gradient norm of the target function relative to its input. GPWAPPO is superior to the previous methods in visual transfer and triumphantly transfers strategies across Visual Cartpole and 16 OpenAI Procgen domains.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".