FAITH: Feasibility-Aware Safety-Filtered RL for High-Dimensional Systems
We present FAITH, a feasibility-aware, model-free framework that approximates the optimal state-action safety value and amortizes minimal-intervention filtering with a feedforward network.
ProofPaper ↗
Key points
- Safe reinforcement learning commonly places safety and task performance in the same policy objective, where they can introduce competing updates.
- Safety filters separate them at action execution, but classical designs require an analytic safety function and dynamics model, and standard minimal-intervention filters are myopic to long-horizon task return because they minimize only instantaneous action deviation.
- The task policy optimizes the task return through the filtered dynamics, which recovers the feasible constrained problem without a competing safety term in the task-policy update.
- On a 29-DoF humanoid, it reaches a 99.95% safety rate while retaining 97% of the unfiltered return in Walking-Avoid, and obtains the highest measured safety rate in Push-Avoid by learning to sacrifice balancing and fall away from the protected region.
Sources (1)
- [1]FAITH: Feasibility-Aware Safety-Filtered RL for High-Dimensional SystemsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 05:57 PM
We present FAITH, a feasibility-aware, model-free framework that approximates the optimal state-action safety value and amortizes minimal-intervention filtering with a feedforward network.
Safe reinforcement learning commonly places safety and task performance in the same policy objective, where they can introduce competing updates.
Extractive summary: sentences quoted from the sources.