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Scalable Bilinear Pi Learning Using State and Action Features

ICML 2018 • Yi-Chen Chen • Lihong Li • Mengdi Wang

Approximate linear programming (ALP) represents one of the major algorithmic families to solve large-scale Markov decision processes (MDP). In this work, we study a primal-dual formulation of the ALP, and develop a scalable, model-free algorithm called bilinear $\pi$ learning for reinforcement learning when a sampling oracle is provided... (read more)

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