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About this paper
Simon S. Du, Sham M. Kakade, Jason D. Lee, Shachar Lovett, Gaurav Mahajan, Wen Sun, Ruosong Wang
Bilinear Classes provides a framework for learning policies in large-state/action reinforcement-learning problems. It links Bellman inconsistency to policy quality and estimates candidate errors from shared data. Under assumptions including linear representations of both optimal action-value and state-value functions, its algorithm guarantees an ε-optimal policy with polynomial trajectory sample complexity. This is theoretical and requires stated norm and generalization conditions.
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