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A Pontryagin Perspective on Reinforcement Learning

A Pontryagin Perspective on Reinforcement Learning

28 May 2024
Onno Eberhard
Claire Vernade
Michael Muehlebach
ArXivPDFHTML

Papers citing "A Pontryagin Perspective on Reinforcement Learning"

14 / 14 papers shown
Title
Probabilistic Pontryagin's Maximum Principle for Continuous-Time Model-Based Reinforcement Learning
Probabilistic Pontryagin's Maximum Principle for Continuous-Time Model-Based Reinforcement Learning
D. Leeftink
Çağatay Yıldız
Steffen Ridderbusch
Max Hinne
Marcel van Gerven
56
0
0
03 Apr 2025
GVFs in the Real World: Making Predictions Online for Water Treatment
GVFs in the Real World: Making Predictions Online for Water Treatment
Muhammad Kamran Janjua
Haseeb Shah
Martha White
Erfan Miahi
Marlos C. Machado
Adam White
AI4CE
50
8
0
04 Dec 2023
Myriad: a real-world testbed to bridge trajectory optimization and deep
  learning
Myriad: a real-world testbed to bridge trajectory optimization and deep learning
Nikolaus H. R. Howe
Simon Dufort-Labbé
Nitarshan Rajkumar
Pierre-Luc Bacon
42
5
0
22 Feb 2022
Deep physical neural networks enabled by a backpropagation algorithm for
  arbitrary physical systems
Deep physical neural networks enabled by a backpropagation algorithm for arbitrary physical systems
Logan G. Wright
Tatsuhiro Onodera
Martin M. Stein
Tianyu Wang
Darren T. Schachter
Zoey Hu
Peter L. McMahon
PINN
AI4CE
62
483
0
27 Apr 2021
Implicit energy regularization of neural ordinary-differential-equation
  control
Implicit energy regularization of neural ordinary-differential-equation control
Lucas Böttcher
Nino Antulov-Fantulin
Thomas Asikis
36
68
0
11 Mar 2021
Sample-efficient Cross-Entropy Method for Real-time Planning
Sample-efficient Cross-Entropy Method for Real-time Planning
Cristina Pinneri
Shambhuraj Sawant
Sebastian Blaes
Jan Achterhold
Joerg Stueckler
Michal Rolínek
Georg Martius
44
99
0
14 Aug 2020
Pontryagin Differentiable Programming: An End-to-End Learning and
  Control Framework
Pontryagin Differentiable Programming: An End-to-End Learning and Control Framework
Wanxin Jin
Zhaoran Wang
Zhuoran Yang
Shaoshuai Mou
49
78
0
30 Dec 2019
Differentiable Volumetric Rendering: Learning Implicit 3D
  Representations without 3D Supervision
Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision
Michael Niemeyer
L. Mescheder
Michael Oechsle
Andreas Geiger
3DH
3DV
60
989
0
16 Dec 2019
Soft Actor-Critic Algorithms and Applications
Soft Actor-Critic Algorithms and Applications
Tuomas Haarnoja
Aurick Zhou
Kristian Hartikainen
George Tucker
Sehoon Ha
...
Vikash Kumar
Henry Zhu
Abhishek Gupta
Pieter Abbeel
Sergey Levine
105
2,391
0
13 Dec 2018
Learning Latent Dynamics for Planning from Pixels
Learning Latent Dynamics for Planning from Pixels
Danijar Hafner
Timothy Lillicrap
Ian S. Fischer
Ruben Villegas
David R Ha
Honglak Lee
James Davidson
BDL
73
1,416
0
12 Nov 2018
A Tour of Reinforcement Learning: The View from Continuous Control
A Tour of Reinforcement Learning: The View from Continuous Control
Benjamin Recht
54
623
0
25 Jun 2018
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement
  Learning with a Stochastic Actor
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
Tuomas Haarnoja
Aurick Zhou
Pieter Abbeel
Sergey Levine
227
8,236
0
04 Jan 2018
Training Deep Spiking Neural Networks using Backpropagation
Training Deep Spiking Neural Networks using Backpropagation
Junhaeng Lee
T. Delbruck
Michael Pfeiffer
66
940
0
31 Aug 2016
Optimization Methods for Large-Scale Machine Learning
Optimization Methods for Large-Scale Machine Learning
Léon Bottou
Frank E. Curtis
J. Nocedal
173
3,198
0
15 Jun 2016
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