Search Results for author: Rein Houthooft

Found 9 papers, 7 papers with code

The Importance of Sampling inMeta-Reinforcement Learning

no code implementations NeurIPS 2018 Bradly Stadie, Ge Yang, Rein Houthooft, Peter Chen, Yan Duan, Yuhuai Wu, Pieter Abbeel, Ilya Sutskever

Results are presented on a new environment we call `Krazy World': a difficult high-dimensional gridworld which is designed to highlight the importance of correctly differentiating through sampling distributions in meta-reinforcement learning.

Meta Reinforcement Learning reinforcement-learning +1

#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 +2

InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets

37 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

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 Reinforcement Learning (RL) +1

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.

Action Triplet Recognition Atari Games +4

Integrated Inference and Learning of Neural Factors in Structural Support Vector Machines

no code implementations3 Aug 2015 Rein Houthooft, Filip De Turck

To improve prediction accuracy, this paper proposes: (i) Joint inference and learning by integration of back-propagation and loss-augmented inference in SSVM subgradient descent; (ii) Extending SSVM factors to neural networks that form highly nonlinear functions of input features.

Image Segmentation Semantic Segmentation +1

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