Pieter Abbeel

A K-fold Method for Baseline Estimation in Policy Gradient Algorithms

Nithyanand Kota, Abhishek Mishra, Sunil Srinivasa, Xi, Chen, Pieter Abbeel

A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models

Chelsea Finn, Paul Christiano, Pieter Abbeel, Sergey Levine

Comments NIPS 2016 Workshop on Adversarial Training. First two authors contributed equally

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

Yan Duan, John Schulman, Xi Chen, Peter L. Bartlett, Ilya Sutskever, Pieter Abbeel

Comments 14 pages. Under review as a conference paper at ICLR 2017

Reset-Free Guided Policy Search: Efficient Deep Reinforcement Learning with Stochastic Initial States

William Montgomery, Anurag Ajay, Chelsea Finn, Pieter Abbeel, Sergey Levine

Combinatorial Energy Learning for Image Segmentation

Jeremy Maitin-Shepard, Viren Jain, Michal Januszewski, Peter Li, Pieter Abbeel

Learning Modular Neural Network Policies for Multi-Task and Multi-Robot Transfer

Coline Devin, Abhishek Gupta, Trevor Darrell, Pieter Abbeel, Sergey Levine

Comments Under review at the International Conference on Robotics and Automation (ICRA) 2017

Toward a Science of Autonomy for Physical Systems: Paths

Pieter Abbeel, Ken Goldberg, Gregory Hager, Julie Shah

Comments A Computing Community Consortium (CCC) white paper, 10 pages

One-Shot Learning of Manipulation Skills with Online Dynamics Adaptation and Neural Network Priors

Justin Fu, Sergey Levine, Pieter Abbeel

InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets

Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, Pieter Abbeel

Benchmarking Deep Reinforcement Learning for Continuous Control

Yan Duan, Xi Chen, Rein Houthooft, John Schulman, Pieter Abbeel

Comments 14 pages, ICML 2016

Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization

Chelsea Finn, Sergey Levine, Pieter Abbeel

Comments International Conference on Machine Learning (ICML), 2016, to appear

End-to-End Training of Deep Visuomotor Policies

Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel

Comments updating with revisions for JMLR final version

Model-based Reinforcement Learning with Parametrized Physical Models and Optimism-Driven Exploration

Christopher Xie, Sachin Patil, Teodor Moldovan, Sergey Levine, Pieter Abbeel

Comments 8 pages

Deep Spatial Autoencoders for Visuomotor Learning

Chelsea Finn, Xin Yu Tan, Yan Duan, Trevor Darrell, Sergey Levine, Pieter Abbeel

Comments Published in the International Conference on Robotics and Automation (ICRA)

Learning Deep Control Policies for Autonomous Aerial Vehicles with MPC-Guided Policy Search

Tianhao Zhang, Gregory Kahn, Sergey Levine, Pieter Abbeel

Gradient Estimation Using Stochastic Computation Graphs

John Schulman, Nicolas Heess, Theophane Weber, Pieter Abbeel

Comments Advances in Neural Information Processing Systems 28 (NIPS 2015)

Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models

Bradly C. Stadie, Sergey Levine, Pieter Abbeel

Learning Deep Neural Network Policies with Continuous Memory States

Marvin Zhang, Zoe McCarthy, Chelsea Finn, Sergey Levine, Pieter Abbeel

The path inference filter: model-based low-latency map matching of probe vehicle data

Timothy Hunter, Pieter Abbeel, Alexandre Bayen

Comments Preprint, 23 pages and 23 figures

Learning Contact-Rich Manipulation Skills with Guided Policy Search

Sergey Levine, Nolan Wagener, Pieter Abbeel

Journal ref S. Levine, N. Wagener, P. Abbeel, "Learning Contact-Rich Manipulation Skills with Guided Policy Search," in International Conference on Robotics and Automation (ICRA), 2015

Arriving on time: estimating travel time distributions on large-scale road networks

Timothy Hunter, Aude Hofleitner, Jack Reilly, Walid Krichene, Jerome Thai, Anastasios Kouvelas, Pieter Abbeel, Alexandre Bayen

Discriminative Probabilistic Models for Relational Data

Ben Taskar, Pieter Abbeel, Daphne Koller

Comments Appears in Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence (UAI2002)

Large Scale Estimation in Cyberphysical Systems using Streaming Data: a Case Study with Smartphone Traces

Timothy Hunter, Tathagata Das, Matei Zaharia, Pieter Abbeel, Alexandre M. Bayen

Safe Exploration in Markov Decision Processes

Teodor Mihai Moldovan, Pieter Abbeel

Learning Factor Graphs in Polynomial Time & Sample Complexity

Pieter Abbeel, Daphne Koller, Andrew Y. Ng

Comments Appears in Proceedings of the Twenty-First Conference on Uncertainty in Artificial Intelligence (UAI2005)