AI RESEARCH
Live LTL Progress Tracking: Towards Task-Based Exploration
arXiv CS.LG
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ArXi:2604.17106v1 Announce Type: new Motivated by the challenge presented by non-Markovian objectives in reinforcement learning (RL), we present a novel framework to track and represent the progress of autonomous agents through complex, multi-stage tasks. Given a specification in finite linear temporal logic (LTL), the framework establishes a 'tracking vector' which updates at each time step in a trajectory rollout.