引用次数
引用: 77,539
H-指数: 55
I10-指数 : 72
出版物: 91
High-Dimensional Continuous Control Using Generalized Advantage Estimation
John Schulman , Philipp Moritz , Sergey Levine , Michael Jordan
arXiv: Learning
2,460
2015
Gradient estimation using stochastic computation graphs
Pieter Abbeel , John Schulman , Nicolas Heess , Theophane Weber
neural information processing systems 28 3528 -3536
374
2015
Trust Region Policy Optimization
John Schulman ,
international conference on machine learning 1889 -1897
6,019
2015
Static charging: old and new mysteries
John Schulman
2010
Benchmarking Deep Reinforcement Learning for Continuous Control
Rein Houthooft , Pieter Abbeel , Yan Duan , John Schulman
arXiv: Learning
1,659
2016
Theano: A Python framework for fast computation of mathematical expressions
Rami Al-Rfou , Guillaume Alain , Amjad Almahairi , Christof Angermueller
arXiv: Symbolic Computation
868
2016
VIME: Variational Information Maximizing Exploration
Rein Houthooft , Pieter Abbeel , Filip De Turck , Yan Duan
arXiv: Learning
702
2016
Concrete Problems in AI Safety
Dario Amodei , Jacob Steinhardt , John Schulman , Chris Olah
arXiv: Artificial Intelligence
1,862
2016
Variational Lossy Autoencoder
Ilya Sutskever , Pieter Abbeel , Tim Salimans , Diederik P. Kingma
international conference on learning representations
651
2016
Equivalence Between Policy Gradients and Soft Q-Learning
Pieter Abbeel , John Schulman , Xi Chen
arXiv: Learning
255
2017
Proximal Policy Optimization Algorithms
John Schulman , Alec Radford , Prafulla Dhariwal , Filip Wolski
arXiv: Learning
10,510
2017
Meta Learning Shared Hierarchies
Pieter Abbeel , John Schulman , Jonathan Ho , Kevin Frans
arXiv: Learning
346
2017
UCB Exploration via Q-Ensembles
Pieter Abbeel , Richard Y. Chen , John Schulman , Szymon Sidor
arXiv: Learning
87
2017
Optimizing Expectations: From Deep Reinforcement Learning to Stochastic Computation Graphs
John Schulman
55
2016
On First-Order Meta-Learning Algorithms.
John Schulman , Joshua Achiam , Alex Nichol
arXiv: Learning
1,550
2018
Quantifying Generalization in Reinforcement Learning
John Schulman , Taehoon Kim , Oleg Klimov , Karl Cobbe
arXiv: Learning
476
2018
Policy Gradient Search: Online Planning and Expert Iteration without Search Trees
Tim Salimans , Robert Nishihara , Thomas Anthony , Philipp Moritz
arXiv: Learning
28
2019
The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors.
Ruslan Salakhutdinov , Sam Devlin , John Schulman , Sharada P. Mohanty
arXiv: Learning
21
2021
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning
Rein Houthooft , Pieter Abbeel , Filip De Turck , Yan Duan
arXiv: Artificial Intelligence
521
2016
Model-Based Reinforcement Learning via Meta-Policy Optimization
Pieter Abbeel , Tamim Asfour , John Schulman , Ignasi Clavera
arXiv: Learning
214
2018