Live LTL Progress Tracking: Towards Task-Based Exploration

2026-04-18

AI Summary

This work addresses the challenge of modeling and tracking non-Markovian task objectives in reinforcement learning by proposing a real-time progress tracking framework grounded in finite Linear Temporal Logic (LTL). It introduces, for the first time, a dynamically updatable LTL tracking vector that, combined with a state-labeling mechanism (true/false/open) during trajectory replay, enables fine-grained characterization of execution states in multi-stage complex tasks. The resulting Live LTL Progress Tracking algorithm not only facilitates efficient exploration and reward shaping but also offers a novel pathway for task performance evaluation and discovery of diverse solution strategies. This approach substantially enhances the tractability and scalability of non-Markovian tasks within reinforcement learning settings.

Technology Category

• Machine Learning: Reinforcement Learning
• Planning, Routing, and Scheduling: Planning with Markov Models (MDPs, POMDPs)
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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 trajectory, providing a potential tool for new performance metrics, diverse exploration, and reward shaping. In this paper, we formally present the framework and algorithm, collectively named Live LTL Progress Tracking, give a simple working example, and demonstrate avenues for its integration into RL models. Future work will apply the framework to problems such as task-space exploration and diverse solution-finding in RL.

Problem

• non-Markovian objectives
• reinforcement learning
• linear temporal logic
• task progress tracking
• multi-stage tasks

Innovation

• Live LTL Progress Tracking
• non-Markovian objectives
• linear temporal logic
• tracking vector
• task-based exploration