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. 2311.04014
  4. Cited By
A Method to Improve the Performance of Reinforcement Learning Based on
  the Y Operator for a Class of Stochastic Differential Equation-Based
  Child-Mother Systems
v1v2v3 (latest)

A Method to Improve the Performance of Reinforcement Learning Based on the Y Operator for a Class of Stochastic Differential Equation-Based Child-Mother Systems

7 November 2023
Cheng Yin
Yi Chen
ArXiv (abs)PDFHTML

Papers citing "A Method to Improve the Performance of Reinforcement Learning Based on the Y Operator for a Class of Stochastic Differential Equation-Based Child-Mother Systems"

10 / 10 papers shown
Title
Deep Reinforcement Learning for Online Control of Stochastic Partial
  Differential Equations
Deep Reinforcement Learning for Online Control of Stochastic Partial Differential Equations
Erfan Pirmorad
Faraz Khoshbakhtian
Farnam Mansouri
A. Farahmand
AI4CE
43
6
0
21 Oct 2021
Safe Learning in Robotics: From Learning-Based Control to Safe
  Reinforcement Learning
Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning
Lukas Brunke
Melissa Greeff
Adam W. Hall
Zhaocong Yuan
Siqi Zhou
Jacopo Panerati
Angela P. Schoellig
OffRL
63
629
0
13 Aug 2021
Infinitely Deep Bayesian Neural Networks with Stochastic Differential
  Equations
Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations
Winnie Xu
Ricky T. Q. Chen
Xuechen Li
David Duvenaud
BDLUQCV
70
49
0
12 Feb 2021
Recovery RL: Safe Reinforcement Learning with Learned Recovery Zones
Recovery RL: Safe Reinforcement Learning with Learned Recovery Zones
Brijen Thananjeyan
Ashwin Balakrishna
Suraj Nair
Michael Luo
K. Srinivasan
M. Hwang
Joseph E. Gonzalez
Julian Ibarz
Chelsea Finn
Ken Goldberg
OffRL
80
229
0
29 Oct 2020
Responsive Safety in Reinforcement Learning by PID Lagrangian Methods
Responsive Safety in Reinforcement Learning by PID Lagrangian Methods
Adam Stooke
Joshua Achiam
Pieter Abbeel
81
300
0
08 Jul 2020
Deep Neural Network Framework Based on Backward Stochastic Differential
  Equations for Pricing and Hedging American Options in High Dimensions
Deep Neural Network Framework Based on Backward Stochastic Differential Equations for Pricing and Hedging American Options in High Dimensions
Yangang Chen
J. Wan
46
61
0
25 Sep 2019
Exploration versus exploitation in reinforcement learning: a stochastic
  control approach
Exploration versus exploitation in reinforcement learning: a stochastic control approach
Haoran Wang
T. Zariphopoulou
X. Zhou
76
49
0
04 Dec 2018
Proximal Policy Optimization Algorithms
Proximal Policy Optimization Algorithms
John Schulman
Filip Wolski
Prafulla Dhariwal
Alec Radford
Oleg Klimov
OffRL
535
19,265
0
20 Jul 2017
Asynchronous Methods for Deep Reinforcement Learning
Asynchronous Methods for Deep Reinforcement Learning
Volodymyr Mnih
Adria Puigdomenech Badia
M. Berk Mirza
Alex Graves
Timothy Lillicrap
Tim Harley
David Silver
Koray Kavukcuoglu
207
8,879
0
04 Feb 2016
Continuous control with deep reinforcement learning
Continuous control with deep reinforcement learning
Timothy Lillicrap
Jonathan J. Hunt
Alexander Pritzel
N. Heess
Tom Erez
Yuval Tassa
David Silver
Daan Wierstra
325
13,286
0
09 Sep 2015
1