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. 2109.10552
  4. Cited By
MEPG: A Minimalist Ensemble Policy Gradient Framework for Deep
  Reinforcement Learning

MEPG: A Minimalist Ensemble Policy Gradient Framework for Deep Reinforcement Learning

22 September 2021
Qiang He
Yuxun Qu
Chen Gong
Xinwen Hou
    OffRL
ArXivPDFHTML

Papers citing "MEPG: A Minimalist Ensemble Policy Gradient Framework for Deep Reinforcement Learning"

6 / 6 papers shown
Title
Ensemble Reinforcement Learning: A Survey
Ensemble Reinforcement Learning: A Survey
Yanjie Song
Ponnuthurai Nagaratnam Suganthan
Witold Pedrycz
Junwei Ou
Yongming He
Y. Chen
Yutong Wu
OffRL
46
39
0
05 Mar 2023
Improving Deep Policy Gradients with Value Function Search
Improving Deep Policy Gradients with Value Function Search
Enrico Marchesini
Chris Amato
26
9
0
20 Feb 2023
BAFFLE: Hiding Backdoors in Offline Reinforcement Learning Datasets
BAFFLE: Hiding Backdoors in Offline Reinforcement Learning Datasets
Chen Gong
Zhou Yang
Yunru Bai
Junda He
Jieke Shi
...
Arunesh Sinha
Bowen Xu
Xinwen Hou
David Lo
Guoliang Fan
AAML
OffRL
21
7
0
07 Oct 2022
Frustratingly Easy Regularization on Representation Can Boost Deep
  Reinforcement Learning
Frustratingly Easy Regularization on Representation Can Boost Deep Reinforcement Learning
Qiang He
Huangyuan Su
Jieyu Zhang
Xinwen Hou
OOD
OffRL
25
6
0
29 May 2022
Dropout Q-Functions for Doubly Efficient Reinforcement Learning
Dropout Q-Functions for Doubly Efficient Reinforcement Learning
Takuya Hiraoka
Takahisa Imagawa
Taisei Hashimoto
Takashi Onishi
Yoshimasa Tsuruoka
11
104
0
05 Oct 2021
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
285
9,145
0
06 Jun 2015
1