Beyond Hard Constraints: Budget-Conditioned Reachability For Safe Offline Reinforcement Learning

ArXi:2603.22292v1 Announce Type: cross Sequential decision making using Marko Decision Process underpins many realworld applications. Both model-based and model free methods have achieved strong results in these settings. However, real-world tasks must balance reward maximization with safety constraints, often conflicting objectives, that can lead to unstable min/max, adversarial optimization. A promising alternative is safety reachability analysis, which precomputes a forward-invariant safe state, action set, ensuring that an agent starting inside this set remains safe indefinitely.