Search Results for author: John Schulman

Found 33 papers, 21 papers with code

Training Verifiers to Solve Math Word Problems

2 code implementations27 Oct 2021 Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, John Schulman

State-of-the-art language models can match human performance on many tasks, but they still struggle to robustly perform multi-step mathematical reasoning.

Mathematical Reasoning

Batch size-invariance for policy optimization

1 code implementation1 Oct 2021 Jacob Hilton, Karl Cobbe, John Schulman

We say an algorithm is batch size-invariant if changes to the batch size can largely be compensated for by changes to other hyperparameters.

Unsolved Problems in ML Safety

no code implementations28 Sep 2021 Dan Hendrycks, Nicholas Carlini, John Schulman, Jacob Steinhardt

Machine learning (ML) systems are rapidly increasing in size, are acquiring new capabilities, and are increasingly deployed in high-stakes settings.

Measuring Sample Efficiency and Generalization in Reinforcement Learning Benchmarks: NeurIPS 2020 Procgen Benchmark

no code implementations29 Mar 2021 Sharada Mohanty, Jyotish Poonganam, Adrien Gaidon, Andrey Kolobov, Blake Wulfe, Dipam Chakraborty, Gražvydas Šemetulskis, João Schapke, Jonas Kubilius, Jurgis Pašukonis, Linas Klimas, Matthew Hausknecht, Patrick MacAlpine, Quang Nhat Tran, Thomas Tumiel, Xiaocheng Tang, Xinwei Chen, Christopher Hesse, Jacob Hilton, William Hebgen Guss, Sahika Genc, John Schulman, Karl Cobbe

We present the design of a centralized benchmark for Reinforcement Learning which can help measure Sample Efficiency and Generalization in Reinforcement Learning by doing end to end evaluation of the training and rollout phases of thousands of user submitted code bases in a scalable way.

The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors

no code implementations26 Jan 2021 William H. Guss, Mario Ynocente Castro, Sam Devlin, Brandon Houghton, Noboru Sean Kuno, Crissman Loomis, Stephanie Milani, Sharada Mohanty, Keisuke Nakata, Ruslan Salakhutdinov, John Schulman, Shinya Shiroshita, Nicholay Topin, Avinash Ummadisingu, Oriol Vinyals

Although deep reinforcement learning has led to breakthroughs in many difficult domains, these successes have required an ever-increasing number of samples, affording only a shrinking segment of the AI community access to their development.

Decision Making Efficient Exploration +1

Phasic Policy Gradient

2 code implementations9 Sep 2020 Karl Cobbe, Jacob Hilton, Oleg Klimov, John Schulman

We introduce Phasic Policy Gradient (PPG), a reinforcement learning framework which modifies traditional on-policy actor-critic methods by separating policy and value function training into distinct phases.

Leveraging Procedural Generation to Benchmark Reinforcement Learning

6 code implementations ICML 2020 Karl Cobbe, Christopher Hesse, Jacob Hilton, John Schulman

We introduce Procgen Benchmark, a suite of 16 procedurally generated game-like environments designed to benchmark both sample efficiency and generalization in reinforcement learning.

Semi-Supervised Learning by Label Gradient Alignment

no code implementations6 Feb 2019 Jacob Jackson, John Schulman

We then formulate an optimization problem whose objective is to minimize the distance between the labeled and the unlabeled data in this space, and we solve it by gradient descent on the imputed labels.

General Classification

Model-Based Reinforcement Learning via Meta-Policy Optimization

no code implementations14 Sep 2018 Ignasi Clavera, Jonas Rothfuss, John Schulman, Yasuhiro Fujita, Tamim Asfour, Pieter Abbeel

Finally, we demonstrate that our approach is able to match the asymptotic performance of model-free methods while requiring significantly less experience.

Model-based Reinforcement Learning

Gotta Learn Fast: A New Benchmark for Generalization in RL

3 code implementations10 Apr 2018 Alex Nichol, Vicki Pfau, Christopher Hesse, Oleg Klimov, John Schulman

In this report, we present a new reinforcement learning (RL) benchmark based on the Sonic the Hedgehog (TM) video game franchise.

Few-Shot Learning Transfer Learning

On First-Order Meta-Learning Algorithms

12 code implementations8 Mar 2018 Alex Nichol, Joshua Achiam, John Schulman

This paper considers meta-learning problems, where there is a distribution of tasks, and we would like to obtain an agent that performs well (i. e., learns quickly) when presented with a previously unseen task sampled from this distribution.

Few-Shot Image Classification

Meta Learning Shared Hierarchies

2 code implementations ICLR 2018 Kevin Frans, Jonathan Ho, Xi Chen, Pieter Abbeel, John Schulman

We develop a metalearning approach for learning hierarchically structured policies, improving sample efficiency on unseen tasks through the use of shared primitives---policies that are executed for large numbers of timesteps.

Legged Robots Meta-Learning

Proximal Policy Optimization Algorithms

134 code implementations20 Jul 2017 John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, Oleg Klimov

We propose a new family of policy gradient methods for reinforcement learning, which alternate between sampling data through interaction with the environment, and optimizing a "surrogate" objective function using stochastic gradient ascent.

Dota 2 Policy Gradient Methods

Teacher-Student Curriculum Learning

3 code implementations1 Jul 2017 Tambet Matiisen, Avital Oliver, Taco Cohen, John Schulman

We propose Teacher-Student Curriculum Learning (TSCL), a framework for automatic curriculum learning, where the Student tries to learn a complex task and the Teacher automatically chooses subtasks from a given set for the Student to train on.

Curriculum Learning Minecraft

UCB Exploration via Q-Ensembles

no code implementations ICLR 2018 Richard Y. Chen, Szymon Sidor, Pieter Abbeel, John Schulman

We show how an ensemble of $Q^*$-functions can be leveraged for more effective exploration in deep reinforcement learning.


Equivalence Between Policy Gradients and Soft Q-Learning

no code implementations21 Apr 2017 John Schulman, Xi Chen, Pieter Abbeel

A partial explanation may be that $Q$-learning methods are secretly implementing policy gradient updates: we show that there is a precise equivalence between $Q$-learning and policy gradient methods in the setting of entropy-regularized reinforcement learning, that "soft" (entropy-regularized) $Q$-learning is exactly equivalent to a policy gradient method.

Policy Gradient Methods Q-Learning

#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning

3 code implementations NeurIPS 2017 Haoran Tang, Rein Houthooft, Davis Foote, Adam Stooke, Xi Chen, Yan Duan, John Schulman, Filip De Turck, Pieter Abbeel

In this work, we describe a surprising finding: a simple generalization of the classic count-based approach can reach near state-of-the-art performance on various high-dimensional and/or continuous deep RL benchmarks.

Atari Games Continuous Control

RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning

15 code implementations9 Nov 2016 Yan Duan, John Schulman, Xi Chen, Peter L. Bartlett, Ilya Sutskever, Pieter Abbeel

The activations of the RNN store the state of the "fast" RL algorithm on the current (previously unseen) MDP.

Variational Lossy Autoencoder

no code implementations8 Nov 2016 Xi Chen, Diederik P. Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, Pieter Abbeel

Representation learning seeks to expose certain aspects of observed data in a learned representation that's amenable to downstream tasks like classification.

Density Estimation Image Generation +1

Concrete Problems in AI Safety

1 code implementation21 Jun 2016 Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, Dan Mané

Rapid progress in machine learning and artificial intelligence (AI) has brought increasing attention to the potential impacts of AI technologies on society.

Safe Exploration

InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets

35 code implementations NeurIPS 2016 Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, Pieter Abbeel

This paper describes InfoGAN, an information-theoretic extension to the Generative Adversarial Network that is able to learn disentangled representations in a completely unsupervised manner.

Image Generation Representation Learning +2

OpenAI Gym

45 code implementations5 Jun 2016 Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, Wojciech Zaremba

OpenAI Gym is a toolkit for reinforcement learning research.

VIME: Variational Information Maximizing Exploration

2 code implementations NeurIPS 2016 Rein Houthooft, Xi Chen, Yan Duan, John Schulman, Filip De Turck, Pieter Abbeel

While there are methods with optimality guarantees in the setting of discrete state and action spaces, these methods cannot be applied in high-dimensional deep RL scenarios.

Continuous Control Variational Inference

Theano: A Python framework for fast computation of mathematical expressions

1 code implementation9 May 2016 The Theano Development Team, Rami Al-Rfou, Guillaume Alain, Amjad Almahairi, Christof Angermueller, Dzmitry Bahdanau, Nicolas Ballas, Frédéric Bastien, Justin Bayer, Anatoly Belikov, Alexander Belopolsky, Yoshua Bengio, Arnaud Bergeron, James Bergstra, Valentin Bisson, Josh Bleecher Snyder, Nicolas Bouchard, Nicolas Boulanger-Lewandowski, Xavier Bouthillier, Alexandre de Brébisson, Olivier Breuleux, Pierre-Luc Carrier, Kyunghyun Cho, Jan Chorowski, Paul Christiano, Tim Cooijmans, Marc-Alexandre Côté, Myriam Côté, Aaron Courville, Yann N. Dauphin, Olivier Delalleau, Julien Demouth, Guillaume Desjardins, Sander Dieleman, Laurent Dinh, Mélanie Ducoffe, Vincent Dumoulin, Samira Ebrahimi Kahou, Dumitru Erhan, Ziye Fan, Orhan Firat, Mathieu Germain, Xavier Glorot, Ian Goodfellow, Matt Graham, Caglar Gulcehre, Philippe Hamel, Iban Harlouchet, Jean-Philippe Heng, Balázs Hidasi, Sina Honari, Arjun Jain, Sébastien Jean, Kai Jia, Mikhail Korobov, Vivek Kulkarni, Alex Lamb, Pascal Lamblin, Eric Larsen, César Laurent, Sean Lee, Simon Lefrancois, Simon Lemieux, Nicholas Léonard, Zhouhan Lin, Jesse A. Livezey, Cory Lorenz, Jeremiah Lowin, Qianli Ma, Pierre-Antoine Manzagol, Olivier Mastropietro, Robert T. McGibbon, Roland Memisevic, Bart van Merriënboer, Vincent Michalski, Mehdi Mirza, Alberto Orlandi, Christopher Pal, Razvan Pascanu, Mohammad Pezeshki, Colin Raffel, Daniel Renshaw, Matthew Rocklin, Adriana Romero, Markus Roth, Peter Sadowski, John Salvatier, François Savard, Jan Schlüter, John Schulman, Gabriel Schwartz, Iulian Vlad Serban, Dmitriy Serdyuk, Samira Shabanian, Étienne Simon, Sigurd Spieckermann, S. Ramana Subramanyam, Jakub Sygnowski, Jérémie Tanguay, Gijs van Tulder, Joseph Turian, Sebastian Urban, Pascal Vincent, Francesco Visin, Harm de Vries, David Warde-Farley, Dustin J. Webb, Matthew Willson, Kelvin Xu, Lijun Xue, Li Yao, Saizheng Zhang, Ying Zhang

Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements.

Dimensionality Reduction General Classification

Benchmarking Deep Reinforcement Learning for Continuous Control

15 code implementations22 Apr 2016 Yan Duan, Xi Chen, Rein Houthooft, John Schulman, Pieter Abbeel

Recently, researchers have made significant progress combining the advances in deep learning for learning feature representations with reinforcement learning.

Atari Games Continuous Control +1

Gradient Estimation Using Stochastic Computation Graphs

1 code implementation NeurIPS 2015 John Schulman, Nicolas Heess, Theophane Weber, Pieter Abbeel

In a variety of problems originating in supervised, unsupervised, and reinforcement learning, the loss function is defined by an expectation over a collection of random variables, which might be part of a probabilistic model or the external world.

High-Dimensional Continuous Control Using Generalized Advantage Estimation

18 code implementations8 Jun 2015 John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, Pieter Abbeel

Policy gradient methods are an appealing approach in reinforcement learning because they directly optimize the cumulative reward and can straightforwardly be used with nonlinear function approximators such as neural networks.

Continuous Control Policy Gradient Methods

Trust Region Policy Optimization

17 code implementations19 Feb 2015 John Schulman, Sergey Levine, Philipp Moritz, Michael. I. Jordan, Pieter Abbeel

We describe an iterative procedure for optimizing policies, with guaranteed monotonic improvement.

Atari Games Policy Gradient Methods

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