论文标题
风险感知的随机路径
Risk-aware Stochastic Shortest Path
论文作者
论文摘要
我们处理马尔可夫决策过程（MDP）上随机最短路径（SSP）的风险感控制问题。通常，对于SSP而言，人们认为期望是遗忘的风险。我们提出了一种替代视图，而是优化有条件的价值风险（CVAR），这是一种已建立的风险度量。我们分别基于线性编程和价值迭代，通过新的见解，两种算法来处理马尔可夫链和MDP。两种算法都提供精确且可证明的正确解决方案。对我们原型实施的评估表明，在几种适度大小的模型上，风险感知控制是可行的。
We treat the problem of risk-aware control for stochastic shortest path (SSP) on Markov decision processes (MDP). Typically, expectation is considered for SSP, which however is oblivious to the incurred risk. We present an alternative view, instead optimizing conditional value-at-risk (CVaR), an established risk measure. We treat both Markov chains as well as MDP and introduce, through novel insights, two algorithms, based on linear programming and value iteration, respectively. Both algorithms offer precise and provably correct solutions. Evaluation of our prototype implementation shows that risk-aware control is feasible on several moderately sized models.
