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首页> 外文期刊>IEEE Transactions on Automatic Control >An Approximate Dynamic Programming Approach to Multiagent Persistent Monitoring in Stochastic Environments With Temporal Logic Constraints
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An Approximate Dynamic Programming Approach to Multiagent Persistent Monitoring in Stochastic Environments With Temporal Logic Constraints

机译：具有时间逻辑约束的随机环境中多主体持久性监视的近似动态规划方法

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摘要

We consider the problem of generating control policies for a team of robots moving in a stochastic environment. The team is required to achieve an optimal surveillance mission, in which a certain “optimizing proposition” needs to be satisfied infinitely often. In addition, a correctness requirement expressed as a temporal logic formula is imposed. By modeling the robots as game transition systems and the environmental elements as Markov chains, the problem reduces to finding an optimal control policy for a Markov decision process, which also satisfies a temporal logic specification. The existing approaches based on dynamic programming are computationally intensive, thus not feasible for large environments and/or large numbers of robots. We propose an approximate dynamic programming (ADP) framework to obtain suboptimal policies with reduced computational complexity. Specifically, we choose a set of basis functions to approximate the optimal costs and find the best approximation through the least-squares method. We also propose a simulation-based ADP approach to further reduce the computational complexity by employing low-dimensional calculations and simulation samples.
机译：我们考虑为在随机环境中移动的一组机器人生成控制策略的问题。要求团队完成最佳监视任务，其中需要无限期地满足某些“优化建议”。另外，提出了表达为时间逻辑公式的正确性要求。通过将机器人建模为游戏过渡系统，将环境要素建模为马尔可夫链，问题减少到为马尔可夫决策过程找到最佳控制策略，这也满足了时间逻辑规范。基于动态编程的现有方法计算量大，因此对于大型环境和/或大量的机器人是不可行的。我们提出了一种近似动态规划（ADP）框架来获得具有降低的计算复杂度的次优策略。具体来说，我们选择一组基函数来近似最优成本，并通过最小二乘法找到最佳近似。我们还提出了一种基于仿真的ADP方法，以通过采用低维计算和仿真样本来进一步降低计算复杂性。

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