ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2310.20380
  4. Cited By
Dropout Strategy in Reinforcement Learning: Limiting the Surrogate
  Objective Variance in Policy Optimization Methods

Dropout Strategy in Reinforcement Learning: Limiting the Surrogate Objective Variance in Policy Optimization Methods

31 October 2023
Zhengpeng Xie
Changdong Yu
Weizheng Qiao
ArXivPDFHTML

Papers citing "Dropout Strategy in Reinforcement Learning: Limiting the Surrogate Objective Variance in Policy Optimization Methods"

3 / 3 papers shown
Title
A Simple and Effective Reinforcement Learning Method for Text-to-Image Diffusion Fine-tuning
Shashank Gupta
Chaitanya Ahuja
Tsung-Yu Lin
Sreya Dutta Roy
Harrie Oosterhuis
Maarten de Rijke
Satya Narayan Shukla
44
1
0
02 Mar 2025
Deep Reinforcement Learning for Autonomous Driving: A Survey
Deep Reinforcement Learning for Autonomous Driving: A Survey
B. R. Kiran
Ibrahim Sobh
V. Talpaert
Patrick Mannion
A. A. Sallab
S. Yogamani
P. Pérez
143
1,599
0
02 Feb 2020
Emergence of Locomotion Behaviours in Rich Environments
Emergence of Locomotion Behaviours in Rich Environments
N. Heess
TB Dhruva
S. Sriram
Jay Lemmon
J. Merel
...
Tom Erez
Ziyun Wang
S. M. Ali Eslami
Martin Riedmiller
David Silver
118
919
0
07 Jul 2017
1