Live LTL Progress Tracking: Towards Task-Based Exploration
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. The values of the vector represent the status of the specification as the trajectory develops, assigning true, false, or 'open' labels (where 'open' is used for indeterminate cases). Applied to an LTL formula tree, the tracking vector can be used to encode detailed information about how a task is executed over a ...
| Authors | Noel Brindise, Cedric Langbort, Melkior Ornik |
|---|---|
| Primary category | cs.LG |
| Published | 18 April 2026 |
| Last updated at source | 18 April 2026 |
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