Theoretical Guarantees of Fictitious Discount Algorithms for Episodic Reinforcement Learning and Global Convergence of Policy Gradient Methods

13 September 2021

Papers citing "Theoretical Guarantees of Fictitious Discount Algorithms for Episodic Reinforcement Learning and Global Convergence of Policy Gradient Methods"

5 / 5 papers shown

Title
On the Effective Horizon of Inverse Reinforcement Learning Yiqing Xu Finale Doshi-Velez David Hsu 51 0 0 21 Feb 2025
Finite-Time Convergence and Sample Complexity of Actor-Critic Multi-Objective Reinforcement Learning Tianchen Zhou Fnu Hairi Haibo Yang Jia-Wei Liu Tian Tong Fan Yang Michinari Momma Yan Gao 43 1 0 05 May 2024
Beyond Stationarity: Convergence Analysis of Stochastic Softmax Policy Gradient Methods Sara Klein Simon Weissmann Leif Döring 29 7 0 04 Oct 2023
A Tale of Sampling and Estimation in Discounted Reinforcement Learning Alberto Maria Metelli Mirco Mutti Marcello Restelli OffRL 27 2 0 11 Apr 2023
Linear convergence of a policy gradient method for some finite horizon continuous time control problems C. Reisinger Wolfgang Stockinger Yufei Zhang 16 5 0 22 Mar 2022