Reinforcement Learning Based Temporal Logic Control with Soft Constraints Using Limit-deterministic Generalized Buchi Automata
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Abstract (arXiv)
This paper studies the control synthesis of motion planning subject to uncertainties. The uncertainties are considered in robot motions and environment properties, giving rise to the probabilistic labeled Markov decision process (PL-MDP). A Model-Free Reinforcement The learning (RL) method is developed to generate a finite-memory control policy to satisfy high-level tasks expressed in linear temporal logic (LTL) formulas. Due to uncertainties and potentially conflicting tasks, this work focuses on infeasible LTL specifications, where a relaxed LTL constraint is developed to allow the agent to revise its motion plan and take violations of original tasks into account for partial satisfaction. And a novel automaton is developed to improve the density of accepting rewards and enable deterministic policies. We proposed an RL framework with rigorous analysis that is guaranteed to achieve multiple objectives in decreasing order: 1) satisfying the acceptance condition of relaxed product MDP and 2) reducing the violation cost over long-term behaviors. We provide simulation and experimental results to validate the performance.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 12 of its 50 references that are in the map. First posted on arXiv 25 Jan 2021.
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12 of 50 references.
References · 12
- 2019Reinforcement Learning for Temporal Logic Control Synthesis with Probabilistic Satisfaction Guaranteescited by 77
- 2018Omega-Regular Objectives in Model-Free Reinforcement Learningcited by 75
- 2019Control Synthesis from Linear Temporal Logic Specifications using Model-Free Reinforcement Learningcited by 75
- 2016Q-Learning for Robust Satisfaction of Signal Temporal Logic Specificationscited by 67
- 2014Probably Approximately Correct MDP Learning and Control With Temporal Logic Constraintscited by 54
- 2014A Learning Based Approach to Control Synthesis of Markov Decision Processes for Linear Temporal Logic Specificationscited by 49
- 2014Verification of Markov Decision Processes using Learning Algorithmscited by 39
- 2018STyLuS*: A Temporal Logic Optimal Control Synthesis Algorithm for Large-Scale Multi-Robot Systemscited by 24
- 2020Reinforcement Learning Based Temporal Logic Control with Maximum Probabilistic Satisfactioncited by 22
- 2020Optimal Probabilistic Motion Planning with Potential Infeasible LTL Constraintscited by 14
- 2017Probabilistic Motion Planning under Temporal Tasks and Soft Constraintscited by 9
- 2020Receding Horizon Control Based Online Motion Planning with Partially Infeasible LTL Specificationscited by 3
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