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Oral: Provably Efficient Safe Exploration via Primal-Dual Policy Optimization

Apr 14, 2021

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DD

Dongsheng Ding

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XW

Xiaohan Wei

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ZY

Zhuoran Yang

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About

We study the Safe Reinforcement Learning (SRL) problem using the Constrained Markov Decision Process (CMDP) formulation in which an agent aims to maximize the expected total reward subject to a safety constraint on the expected total value of a utility function. We focus on an episodic setting with the function approximation where the Markov transition kernels have a linear structure but do not impose any additional assumptions on the sampling model. Designing SRL algorithms with provable comput…

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AISTATS 2021

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The 24th International Conference on Artificial Intelligence and Statistics was held virtually from Tuesday, 13 April 2021 to Thursday, 15 April 2021.

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