研究随机系统中策略的鲁棒性，分析干扰如何破坏最优决策。
Resilient Strategies for Stochastic Systems: How Much Does It Take to Break a Winning Strategy?
- 提出随机环境下策略鲁棒性的量化评估框架
- 针对可达与安全目标，给出干扰下策略失效的数学刻画
- 适用于存在无限次干扰的复杂系统，如自动驾驶
本文研究不确定性下的鲁棒策略问题。鲁棒策略使智能体在扰动下仍能做出可靠决策，尤其关注那些能逆转智能体决策的扰动——例如执行器故障导致指令无法执行。本文引入随机环境中的鲁棒性概念，系统提出一系列基础问题，涵盖具有可达性与安全性目标的马尔可夫决策过程，并可自然扩展至随机博弈。为应对随机性，提出多种扰动累积方式，如期望值或最坏情况；为处理无限次扰动，采用频率等定量指标进行建模。
原文摘要 · Abstract (English)
We study the problem of resilient strategies in the presence of uncertainty. Resilient strategies enable an agent to make decisions that are robust against disturbances. In particular, we are interested in those disturbances that are able to flip a decision made by the agent. Such a disturbance may, for instance, occur when the intended action of the agent cannot be executed due to a malfunction of an actuator in the environment. In this work, we introduce the concept of resilience in the stochastic setting and present a comprehensive set of fundamental problems. Specifically, we discuss such problems for Markov decision processes with reachability and safety objectives, which also smoothly extend to stochastic games. To account for the stochastic setting, we provide various ways of aggregating the amounts of disturbances that may have occurred, for instance, in expectation or in the worst case. Moreover, to reason about infinite disturbances, we use quantitative measures, like their frequency of occurrence.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。