Reinforcement Learning for Signal Temporal Logic using Funnel-Based Approach

Saxena, N and Sandeep, G and Jagtap, P (2023) Reinforcement Learning for Signal Temporal Logic using Funnel-Based Approach. In: UNSPECIFIED, pp. 1-6.

PDF 9th_Ind_con_con_2023. - Published Version Restricted to Registered users only Download (2MB) | Request a copy

Abstract

Signal Temporal Logic (STL) is a powerful framework for describing the complex temporal and logical behaviour of the dynamical system. Several works propose a method to find a controller for the satisfaction of STL specification using reinforcement learning but fail to address either the issue of robust satisfaction in continuous state space or ensure the tractability of the approach. In this paper, leveraging the concept of funnel functions, we propose a tractable reinforcement learning algorithm to learn a time-dependent policy for robust satisfaction of STL specification in continuous state space. We demonstrate the utility of our approach on several tasks using a pendulum and mobile robot examples. © 2023 IEEE.

Item Type:	Conference Paper
Publication:	2023 9th Indian Control Conference, ICC 2023 - Proceedings
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	The copyright for this article belongs to Institute of Electrical and Electronics Engineers Inc.
Keywords:	Computer circuits; Dynamical systems; Learning algorithms; Learning systems; Reinforcement learning; Specifications, Continuous State Space; Learn+; Logical behavior; Reinforcement learning algorithms; Reinforcement learnings; Temporal behavior; Temporal logic specifications; Time dependent, Temporal logic
Department/Centre:	Division of Electrical Sciences > Computer Science & Automation Division of Interdisciplinary Sciences > Robert Bosch Centre for Cyber Physical Systems
Date Deposited:	16 May 2024 05:09
Last Modified:	16 May 2024 05:09
URI:	https://eprints.iisc.ac.in/id/eprint/84509

Actions (login required)

View Item