17.9k citations · 79.2k across the 64 of their papers we have counts for
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Emergent Complexity via Multi-Agent Competition
Trapit Bansal, Jakub Pachocki, Szymon Sidor +2
Reinforcement learning algorithms can train agents that solve problems in complex, interesting environments. Normally, the complexity of the trained agent is closely related to the…
Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments
Maruan Al-Shedivat, Trapit Bansal, Yuri Burda +3
Ability to continuously learn and adapt from limited experience in nonstationary environments is an important milestone on the path towards general intelligence. In this paper, we…
Synthesizing Robust Adversarial Examples
Anish Athalye, Logan Engstrom, Andrew Ilyas +1
Standard methods for generating adversarial examples for neural networks do not consistently fool neural network classifiers in the physical world due to a combination of viewpoint…
An online sequence-to-sequence model for noisy speech recognition
Chung-Cheng Chiu, Dieterich Lawson, Yuping Luo +4
Generative models have long been the dominant approach for speech recognition. The success of these models however relies on the use of sophisticated recipes and complicated machin…
Learning to Generate Reviews and Discovering Sentiment
Alec Radford, Rafal Jozefowicz, Ilya Sutskever
We explore the properties of byte-level recurrent language models. When given sufficient amounts of capacity, training data, and compute time, the representations learned by these…
One-Shot Imitation Learning
Yan Duan, Marcin Andrychowicz, Bradly C. Stadie +5
Imitation learning has been commonly applied to solve different tasks in isolation. This usually requires either careful feature engineering, or a significant number of samples. Th…
Evolution Strategies as a Scalable Alternative to Reinforcement Learning
Tim Salimans, Jonathan Ho, Xi Chen +2
We explore the use of Evolution Strategies (ES), a class of black box optimization algorithms, as an alternative to popular MDP-based RL techniques such as Q-learning and Policy Gr…
Third-Person Imitation Learning
Bradly C. Stadie, Pieter Abbeel, Ilya Sutskever
Reinforcement learning (RL) makes it possible to train agents capable of achieving sophisticated goals in complex and uncertain environments. A key difficulty in reinforcement lear…