A Hierarchical Reinforcement Learning Method For Persistent Time-sensitive Tasks

Abstract

Reinforcement learning has been applied to many interesting problems such as the famous TD-gammon and the inverted helicopter flight. However, little effort has been put into developing methods to learn policies for complex persistent tasks and tasks that are time-sensitive. In this paper, we take a step towards solving this problem by using signal temporal logic (STL) as task specification, and taking advantage of the temporal abstraction feature that the options framework provide. We show via simulation that a relatively easy to implement algorithm that combines STL and options can learn a satisfactory policy with a small number of training cases

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyli2016a

Related papers

• Funnel-based Reward Shaping For Signal Temporal Logic Tasks In Reinforcement Learning (2022)7.16
• TGPO: Temporal Grounded Policy Optimization For Signal Temporal Logic Tasks (2025)0.00
• Optimal Options For Multi-task Reinforcement Learning Under Time Constraints (2020)0.00
• A Policy Search Method For Temporal Logic Specified Reinforcement Learning Tasks (2017)11.58
• Sample-efficient Reinforcement Learning With Temporal Logic Objectives: Leveraging The Task Specification To Guide Exploration (2024)0.00
• Joint Learning Of Policy With Unknown Temporal Constraints For Safe Reinforcement Learning (2023)0.00
• Hierarchical And Interpretable Skill Acquisition In Multi-task Reinforcement Learning (2017)0.00
• Logical Specifications-guided Dynamic Task Sampling For Reinforcement Learning Agents (2024)2.26