基于极限确定性广义b chi自动机的软时间逻辑约束强化学习

Mingyu Cai , Zhangli Zhou , Lin Li , Shaoping Xiao , Zhen Kan
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引用次数: 0

摘要

本文研究了不确定条件下运动规划的控制综合,特别是机器人运动和环境属性,使用概率标记马尔可夫决策过程(PL-MDP)建模。为了解决这个问题,设计了一种无模型强化学习(RL)方法来产生有限内存控制策略,该策略满足线性时间逻辑(LTL)公式指定的复杂任务。认识到存在的不确定性和潜在的冲突目标,本研究的中心是解决不可行的LTL规范。宽松的LTL约束使代理能够调整其运动计划,通过考虑必要的任务违反来实现部分满足。此外,引入了一个新的自动机结构来增加接受奖励的密度,促进确定性的政策结果。提出的RL框架经过严格分析,优先考虑两个关键目标:(1)满足放宽产品MDP的接受条件;(2)最小化长期违规成本。仿真和实验结果验证了该框架的有效性和鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reinforcement learning with soft temporal logic constraints using limit-deterministic generalized Büchi automaton
This paper investigates control synthesis for motion planning under conditions of uncertainty, specifically in robot motion and environmental properties, which are modeled using a probabilistic labeled Markov decision process (PL-MDP). To address this, a model-free reinforcement learning (RL) approach is designed to produce a finite-memory control policy that meets complex tasks specified by linear temporal logic (LTL) formulas. Recognizing the presence of uncertainties and potentially conflicting objectives, this study centers on addressing infeasible LTL specifications. A relaxed LTL constraint enables the agent to adapt its motion plan, allowing for partial satisfaction by accounting for necessary task violations. Additionally, a new automaton structure is introduced to increase the density of accepting rewards, facilitating deterministic policy outcomes. The proposed RL framework is rigorously analyzed and prioritizes two key objectives: (1) satisfying the acceptance condition of the relaxed product MDP, and (2) minimizing long-term violation costs. Simulation and experimental results are presented to demonstrate the framework’s effectiveness and robustness.
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