Solving Hidden-Semi-Markov-Mode Markov Decision Problems

17 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Towards Continual Reinforcement Learning: A Review and Perspectives (2020, methodology): “…or boundaries between significant switches without an oracle as in (Padakandla et al., 2019; Da Silva et al., 2006; Rosman and Ramamoorthy, 2012; Hadoux, Beynier, and Weng, 2014b; Li, Gu, Zhu, and Zhang, 2019; Kessler, Parker-Holder, Ball, Zohren, and Roberts, 2022; Luo, Jiang, Yu, Zhang, and…”
A Survey of Learning in Multiagent Environments: Dealing with Non-Stationarity (2017, methodology): “Hadoux et al. (2014a) proposed an adaptation of RL-CD, replacing the quality measure by statistical tests for change-point detection, yielding RL-CD with sequential change-point detection.”
Sequential Decision-Making under Non-stationary Environments via Sequential Change-point Detection (2014, background): “An other interesting application of this work is learning HS3MDPs, an extension of the HM-MDP where the transitions between the modes are semimarkovian [8].”