[2006.10701] Deep Reinforcement Learning amidst Lifelong Non-Stationarity

As humans, our goals and our environment are persistently changing throughout our lifetime based on our experiences, actions, and internal and external drives. In contrast, typical reinforcement learning problem set-up…

Deep Reinforcement Learning amidst Lifelong Non-Stationarity Annie Xie, James Harrison, Chelsea Finn Stanford University, Stanford, CA {anniexie,jharrison,cbfinn}@stanford.edu Abstract As humans, our goals and our environment are persistently changing throughout our lifetime based on our experiences, actions, and internal and external drives. In contrast, typical reinforcement learning problem set-ups consider decision processes that are stationary across episodes. Can we develop reinforcement learning algorithms that can cope with the persistent change in the former, more realistic problem se

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