John Co-Reyes — accepted AI-conference papers

2018

Self-Consistent Trajectory Autoencoder: Hierarchical Reinforcement Learning with Trajectory Embeddings

John Co-Reyes, YuXuan Liu, Abhishek Gupta, Benjamin Eysenbach, Pieter Abbeel, Sergey Levine

ICML 2018oral

In this work, we take a representation learning perspective on hierarchical reinforcement learning, where the problem of learning lower layers in a hierarchy is transformed into the problem of learning trajectory-level generative models. We show that we can learn continuous latent representations of…

Cited by 193 Source ↗PDF ↗Scholar ↗

2017

EX2: Exploration with Exemplar Models for Deep Reinforcement Learning

Justin Fu, John Co-Reyes, Sergey Levine

NeurIPS 2017spotlight

Deep reinforcement learning algorithms have been shown to learn complex tasks using highly general policy classes. However, sparse reward problems remain a significant challenge. Exploration methods based on novelty detection have been particularly successful in such settings but typically require g…

Cited by 197 Source ↗PDF ↗Scholar ↗