Deep Reinforcement Learning amidst Continual Structured Non-Stationarity

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Towards Continual Reinforcement Learning: A Review and Perspectives (2020, background): “For example, the recent work of (Xie et al., 2021) considers a formulation with Markovian transitions between”
Reactive Exploration to Cope with Non-Stationarity in Lifelong Reinforcement Learning (2022, background): “Prior work has highlighted the importance of correctly re-using collected experiences in off-policy algorithms when non-stationarities are encountered (Liotet et al., 2021; Chandak et al., 2020; Xie et al., 2021).”