Optimism in Reinforcement Learning with Generalized Linear Function Approximation

Yining Wang; Ruosong Wang; Simon S. Du; Akshay Krishnamurthy

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arXiv · 1912.04136

Optimism in Reinforcement Learning with Generalized Linear Function Approximation

Yining Wang·Ruosong Wang·Simon S. Du·Akshay Krishnamurthy

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Abstract

We design a new provably efficient algorithm for episodic reinforcement learning with generalized linear function approximation. We analyze the algorithm under a new expressivity assumption that we call "optimistic closure," which is strictly weaker than assumptions from prior analyses for the linear setting. With optimistic closure, we prove that our algorithm enjoys a regret bound of $\tilde{O}(\sqrt{d^3 T})$ where $d$ is the dimensionality of the state-action features and $T$ is the number of episodes. This is the first statistically and computationally efficient algorithm for reinforcement learning with generalized linear functions.

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BibTeX RIS

Yining Wang, Ruosong Wang, Simon S. Du, Akshay Krishnamurthy. 2019-12-09. Optimism in Reinforcement Learning with Generalized Linear Function Approximation. https://arxiv.org/abs/1912.04136

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