Safe Reinforcement Learning via Observation Shielding
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Reinforcement Learning (RL) algorithms have shown success in scaling up to large problems. However, deploying those algorithms in real-world applications remains challenging due to their vulnerability to adversarial perturbations. Existing RL robustness methods against adversarial attacks are weak to large perturbations - a scenario that cannot be ruled out for RL adversarial threats, as is the case for deep neural networks in classification tasks. This paper proposes a method called observation-shielding RL (OSRL) to increase the robustness of RL against large perturbations using predictive models and threat detection. Instead of changing the RL algorithms with robustness regularization or retrain them with adversarial perturbations, we depart considerably from previous approaches and develop an add-on safety feature for existing RL algorithms during runtime. OSRL builds on the idea of model predictive shielding, where an observation predictive model is used to override the perturbed observations as needed to ensure safety. Extensive experiments on various MuJoCo environments (Ant, Hooper) and the classical pendulum environment demonstrate that our proposed OSRL is safer and more efficient than state-of-the-art robustness methods under large perturbations.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.015 | 0.003 |
| 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