论文标题
通过加强学习在随机定向图中的可及性分析
Reachability analysis in stochastic directed graphs by reinforcement learning
论文作者
论文摘要
我们通过加强学习方法来表征随机定向图中的可及性概率。特别是，我们表明可以通过差异包含来对随机挖掘中的过渡概率的动力学进行建模，这反过来又可以解释为马尔可夫决策过程。使用后一个框架，我们提供了一种方法来设计奖励功能，以在随机挖掘的一组节点的可及性概率上提供上限和下限。该技术的有效性是通过应用于流动性疾病的扩散而不是移动剂的接近度模式产生的，流行病的传播。
We characterize the reachability probabilities in stochastic directed graphs by means of reinforcement learning methods. In particular, we show that the dynamics of the transition probabilities in a stochastic digraph can be modeled via a difference inclusion, which, in turn, can be interpreted as a Markov decision process. Using the latter framework, we offer a methodology to design reward functions to provide upper and lower bounds on the reachability probabilities of a set of nodes for stochastic digraphs. The effectiveness of the proposed technique is demonstrated by application to the diffusion of epidemic diseases over time-varying contact networks generated by the proximity patterns of mobile agents.
