Graph-based Cross Entropy method for solving multi-robot decentralized POMDPs
Graph-based Cross Entropy method for solving multi-robot decentralized POMDPs
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基于图的交叉熵方法求解多机器人分散 POMDP
DOI：
10.1109/icra.2016.7487751
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
J. Vian
中科院分区：
文献类型：
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作者：
Shayegan Omidshafiei;Ali;Chris Amato;Shih‐Yuan Liu;J. How;J. Vian
本文介绍了一种不确定多机器人决策问题的概率算法，该算法可以看作是一个分散的部分可观测马尔可夫决策过程（Dec-POMDP）。Dec-POMDPs本质上是同步的决策框架，需要大量的计算资源来解决，这使得它们对于许多现实世界的机器人应用来说是不可行的。分散式部分可观测半马尔可夫决策过程（Dec-POSMDP）最近被引入作为Dec-POMDP的扩展，它使用高级宏动作来允许大规模的异步决策。然而，现有的Dec-POSMDP解决方案方法具有有限的可扩展性或随着问题大小的增长而表现不佳。本文提出了一种基于交叉熵的Dec-POSMDP算法的组合优化文献的动机。该算法被应用到一个受约束的包交付域，它显着优于现有的Dec-POSMDP解决方案的方法。
This paper introduces a probabilistic algorithm for multi-robot decision-making under uncertainty, which can be posed as a Decentralized Partially Observable Markov Decision Process (Dec-POMDP). Dec-POMDPs are inherently synchronous decision-making frameworks which require significant computational resources to be solved, making them infeasible for many real-world robotics applications. The Decentralized Partially Observable Semi-Markov Decision Process (Dec-POSMDP) was recently introduced as an extension of the Dec-POMDP that uses high-level macro-actions to allow large-scale, asynchronous decision-making. However, existing Dec-POSMDP solution methods have limited scalability or perform poorly as the problem size grows. This paper proposes a cross-entropy based Dec-POSMDP algorithm motivated by the combinatorial optimization literature. The algorithm is applied to a constrained package delivery domain, where it significantly outperforms existing Dec-POSMDP solution methods.