John Schulman
19 papers · 2013–2024 · 5 venues · across top CS/AI venues
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Achievements
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πΊοΈ Taxonomy Completionist (33) π Academic Marathon Β· Half π Conference Polyglot Β· Silver π Interdisciplinary Bridge Β· Silver π£ Hot Topic Early Bird
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Deep Specialist
(10)
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Mega-Team Β· Squad
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Topic Evolution
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Renaissance Researcher
(6)
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Trend Setter
π₯
Unstoppable Β· Silver
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Conference Pioneer
ποΈ
Keyword Collector
(84)
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Century Club Β· Bronze
Venues
Top co-authors
Research topics
Reinforcement Learning
(13)
Methods
(12)
Machine Learning
(10)
Artificial Intelligence
(9)
Core AI
(7)
Optimization & Theory
(7)
Deep Learning
(6)
Applications
(5)
Learning Types Β· Machine Learning
(4)
Learning Paradigms
(3)
Learning Types Β· Deep Learning
(3)
Application Areas
(2)
Computer Vision
(1)
Mathematics & Optimization
(1)
Natural Language Processing
(1)
Keywords
reinforcement learning
(7)
deep reinforcement learning
(5)
policy gradient
(4)
sample efficiency
(3)
reward model
(3)
data augmentation
(2)
exploration bonus
(2)
continuous control
(2)
neural network
(2)
intrinsic motivation
(2)
reinforcement learning from human feedback
(2)
hierarchical learning
(1)
variational inference
(1)
domain generalization
(1)
policy optimization
(1)
imitation learning
(1)
representation learning
(1)
instruction following
(1)
language model alignment
(1)
policy learning
(1)
Papers
Let's Verify Step by Step
ICLR 2024
Rule Based Rewards for Language Model Safety
NeurIPS 2024
Scaling Laws for Reward Model Overoptimization
ICML 2023
Batch size-invariance for policy optimization
NeurIPS 2022
Training language models to follow instructions with human feedback
NeurIPS 2022
Phasic Policy Gradient
ICML 2021
Distribution Augmentation for Generative Modeling
ICML 2020
Leveraging Procedural Generation to Benchmark Reinforcement Learning
ICML 2020
Quantifying Generalization in Reinforcement Learning
ICML 2019
Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations
RSS 2018
META LEARNING SHARED HIERARCHIES
ICLR 2018
Model-Based Reinforcement Learning via Meta-Policy Optimization
CoRL 2018
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning
NeurIPS 2017
Benchmarking Deep Reinforcement Learning for Continuous Control
ICML 2016
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
NeurIPS 2016
VIME: Variational Information Maximizing Exploration
NeurIPS 2016
Gradient Estimation Using Stochastic Computation Graphs
NeurIPS 2015
Trust Region Policy Optimization
ICML 2015
Finding Locally Optimal, Collision-Free Trajectories with Sequential Convex Optimization
RSS 2013