116 citations · 195 across the 2 of their papers we have counts for
9 papers
Meta Learning Shared Hierarchies
Kevin Frans, Jonathan Ho, Xi Chen +2
We develop a metalearning approach for learning hierarchically structured policies, improving sample efficiency on unseen tasks through the use of shared primitives---policies that…
Proximal Policy Optimization Algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal +2
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 "sur…
Teacher-Student Curriculum Learning
Tambet Matiisen, Avital Oliver, Taco Cohen +1
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…
UCB Exploration via Q-Ensembles
Richard Y. Chen, Szymon Sidor, Pieter Abbeel +1
We show how an ensemble of -functions can be leveraged for more effective exploration in deep reinforcement learning. We build on well established algorithms from the bandit s…
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
Xi Chen, Yan Duan, Rein Houthooft +3
This paper describes InfoGAN, an information-theoretic extension to the Generative Adversarial Network that is able to learn disentangled representations in a completely unsupervis…
OpenAI Gym
Greg Brockman, Vicki Cheung, Ludwig Pettersson +4
OpenAI Gym is a toolkit for reinforcement learning research. It includes a growing collection of benchmark problems that expose a common interface, and a website where people can s…