Risk-Aware Stochastic Shortest Path.

AAAI Conference on Artificial Intelligence(2022)

引用 8|浏览16
暂无评分
摘要
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.
更多
查看译文
关键词
Planning,Routing,And Scheduling (PRS)
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络