TraCeS: Learning Per-Timestep Constraint-Violation Credit from Sparse Trajectory-Level Labels
arXiv:2504.12557v3 Announce Type: replace Abstract: Ensuring safe behavior in reinforcement learning (RL) is challenging when safety constraints are implicit and cannot be densely measured. In many settings, supervision is limited to coarse approvals or rejections of whole trajectories (e.g., whether a rollout remained within an unknown safety threshold). We propose TraCeS (Trajectory-based Constraint Estimation for Safety), a method for learning per-timestep violation credit from such sparse trajectory-level labels. TraCeS trains a sequential violation estimator whose per-step credits factori
This research addresses a fundamental challenge in applying reinforcement learning to safety-critical systems, a prerequisite for broader AI deployment.
Ensuring safe behavior in autonomous systems without dense supervision is critical for public acceptance and regulatory approval of AI in real-world applications.
The ability to learn detailed constraint violations from sparse, trajectory-level feedback significantly reduces the data labeling burden and expands the applicability of safe RL.
- · AI developers
- · Robotics
- · Autonomous vehicle industry
- · Safety-critical AI applications
- · Traditional dense supervision methods
- · AI applications with high labeling costs
More robust and safer deployment of AI systems in complex environments becomes feasible.
Reduced development costs and faster iteration cycles for AI systems requiring safety guarantees accelerate their adoption.
The increased trustworthiness of autonomous AI could lead to new industries and services reliant on pervasive AI agents.
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