A research paper proposes the Redistribution-based Cost Inference (RCI) framework to convert sparse trajectory-level stop-feedback into dense per-step costs for safe offline reinforcement learning. The paper claims the transformation is lossless in theory and demonstrates lower violation rates than sparse and classifier-based baselines on highway driving and robotic manipulation tasks.
Safe offline RL typically assumes dense per-step cost annotations, but practical supervisors often provide only trajectory-level stop-feedback: a binary signal at the first unsafe transition without per-step attribution. The paper frames this as a temporal credit assignment problem and introduces RCI, which uses return decomposition to convert sparse feedback into dense costs, then trains a constrained offline policy. The authors show that return-equivalent redistribution preserves the feasible policy set and optimal Lagrangian in a CMDP, and report better-conditioned cost critic learning and robustness to heterogeneous datasets and label noise.
The core technical contribution is a return-equivalent redistribution method that maps sparse stop-feedback to dense per-step costs while preserving the feasible policy set and optimal Lagrangian in constrained MDPs. This suggests a principled way to handle temporal credit assignment for safety signals, potentially improving cost critic conditioning and reducing violation rates compared to sparse or classifier-based approaches.
The approach addresses a practical gap in deploying safe offline RL where dense cost labels are expensive or unavailable. If validated beyond the reported domains, it could lower annotation costs and improve safety compliance for learned policies in robotics and autonomous driving, making safe RL more feasible for real-world industrial applications.
The method could reduce the cost and effort of collecting dense safety annotations for offline RL, enabling safer policy deployment in robotics and autonomous systems. This may lower barriers to adopting safe RL in industries where trajectory-level feedback is more natural, such as manufacturing or logistics.
Next observable signals include follow-up work applying RCI to other safety-critical domains, open-source code releases, or benchmark results on standard safe RL suites. Potential extensions may explore handling multi-step delayed feedback, continuous safety signals, or integration with model-based offline RL methods.