Sequential Decision-Making under Non-stationary Environments via Sequential Change-point Detection

By Emmanuel Hadoux, Aurélie Beynier, Paul Weng. 2014

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Citations in the corpus

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.”
Deep Reinforcement Learning amidst Continual Structured Non-Stationarity (2021, background): “Algorithms that study the HM-MDP setting aim to quickly detect changes in the environment (Da Silva et al., 2006; Hadoux et al., 2014; Banerjee et al., 2017; Padakandla et al., 2020), while LILAC aims to anticipate future changes and adapt as they happen.”
Deep Reinforcement Learning amidst Lifelong Non-Stationarity (2020, background): “meters. In particular, this notion of infrequent, discrete shifts underlies the changepoint detection literature [1, 13]. Previous work both within sequential decision making in changing environments [9, 21, 4] and meta-learning within changing data streams [24] may enable a version of LILAC capable”
Lifelong Hyper-Policy Optimization with Multiple Importance Sampling Regularization (2021, background): “In this second case, many works focus on the detection of abrupt changes in the dynamics (da Silva et al. 2006; Hadoux, Beynier, and Weng 2014) or on scenarios where the non-stationarity arises from switching between stationary dynamics, where the number of such dynamics is known (Choi, Yeung, and…”
Non-Stationary Markov Decision Processes a Worst-Case Approach using Model-Based Reinforcement Learning (2019, methodology): “Similarly, Wiering [2001], Da Silva et al. [2006], Hadoux et al. [2014] proposed a method tracking the switching occurrence and re-planning if needed.”