Reinforcement Learning
"Reinforcement learning is learning what to do – how to map situations to actions – so as to maximize a numerical reward signal. The learner is not told which actions to take, as in most forms of machine learning, but instead must discover which actions yield the most reward by trying them."
– Sutton, Richard S. and Andrew G. Barto. Reinforcement Learning: An Introduction. (1.1). MIT Press, Cambridge, MA, 1998.
OfflineRLWithoutOff-PolicyEvaluation
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Inaddition, wehypothesize thatthestrong performance of the one-step algorithm is due to a combination of favorable structure in the environmentandbehaviorpolicy.
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Beyond Uniform Sampling: Offline Reinforcement Learning with Imbalanced Datasets
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When the collected dataset is good, i.e., mostly
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2bba9f4124283edd644799e0cecd45ca-Paper.pdf
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The problem of inverse reinforcement learning (IRL) is relevant to a variety of tasks including valuealignment androbot learning fromdemonstration.
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256bf8e6923a52fda8ddf7dc050a1148-Paper.pdf
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One method of obtaining such a representation uses the notion of abisimulation metric(BSM) [13, 14].
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