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Despite groundbreaking progress in reinforcement learning for robotics, gameplay, and other complex domains, major challenges remain in applying reinforcement learning to the evolving, open-world problems often found in critical application spaces.
Lifelong machine learning, second edition
Zhiyuan Chen and Bing Liu · 1939
Earlier work this paper cites.
Catastrophic interference in connectionist networks: The sequential learning problem
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Cited alongside, same era.
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Universal successor features for transfer reinforcement learning
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