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Tsallis Reinforcement Learning: A Unified Framework for Maximum Entropy
  Reinforcement Learning

Tsallis Reinforcement Learning: A Unified Framework for Maximum Entropy Reinforcement Learning

31 January 2019
Kyungjae Lee
Sungyub Kim
Sungbin Lim
Sungjoon Choi
Songhwai Oh
ArXivPDFHTML

Papers citing "Tsallis Reinforcement Learning: A Unified Framework for Maximum Entropy Reinforcement Learning"

5 / 5 papers shown
Title
Divergence-Augmented Policy Optimization
Qing Wang
Yingru Li
Jiechao Xiong
Tong Zhang
OffRL
47
16
0
28 Jan 2025
Decoupling regularization from the action space
Decoupling regularization from the action space
Sobhan Mohammadpour
Emma Frejinger
Pierre-Luc Bacon
37
0
0
10 Jun 2024
Offline RL with No OOD Actions: In-Sample Learning via Implicit Value
  Regularization
Offline RL with No OOD Actions: In-Sample Learning via Implicit Value Regularization
Haoran Xu
Li Jiang
Jianxiong Li
Zhuoran Yang
Zhaoran Wang
Victor Chan
Xianyuan Zhan
OffRL
36
73
0
28 Mar 2023
Maximum Entropy RL (Provably) Solves Some Robust RL Problems
Maximum Entropy RL (Provably) Solves Some Robust RL Problems
Benjamin Eysenbach
Sergey Levine
OOD
50
175
0
10 Mar 2021
Mirror Descent Policy Optimization
Mirror Descent Policy Optimization
Manan Tomar
Lior Shani
Yonathan Efroni
Mohammad Ghavamzadeh
19
83
0
20 May 2020
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