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Jan 23, 2024 · We consider a regularized expected reward optimization problem in the non-oblivious setting that covers many existing problems in reinforcement learning (RL).
Aug 19, 2024 · We consider a regularized expected reward optimization problem in the non-oblivious setting that covers many existing problems in reinforcement learning (RL).
Stochastic proximal gradient method for (nonconcave) regularized expected reward optimization. Improve sample complexity via variance reduction. How to obtain ...
The purpose of this paper is to leverage existing tools and results in MDPs and nonconvex optimization for solving the general regularized expected reward ...
The purpose of this paper is to leverage existing tools and results in MDPs and nonconvex optimization for solving the general regularized expected reward ...
Aug 20, 2024 · This paper presents a novel stochastic proximal gradient method with variance reduction for optimizing regularized expected rewards, a common problem in ...
Accepted papers at TMLR on X: "On the Stochastic (Variance-Reduced ...
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Aug 19, 2024 · On the Stochastic (Variance-Reduced) Proximal Gradient Method for... We consider a regularized expected reward optimization problem in the ...
[2] L. Liang*, H. Yang. On the Stochastic (Variance-Reduced) Proximal Gradient Method for Regularized Expected Reward Optimization. [pdf].
Ling Liang, Haizhao Yang. On the stochastic (variance-reduced) proximal gradient method for regularized expected reward optimization, TMLR 2024. arXiv, TMLR ...
On the Stochastic (Variance-Reduced) Proximal Gradient Method for Regularized Expected Reward Optimization · Ling LiangHaizhao Yang. Computer Science. arXiv.org.