Apprenticeship Learning and Reinforcement Learning with Application to Robotic Control,
Pieter Abbeel
Ph.D. Dissertation, Stanford University, Computer Science, August 2008

[ALL | Deep RL | Unsupervised | Apprentice | Optimization-based Planning | Belief Space Planning | Hierarchical Planning | Perception | Deformable Objects | Medical Robotics | Helicopter | Connectomics ]


PixelSNAIL: An Improved Autoregressive Generative Model,
Xi (Peter) Chen, Nikhil Mishra, Mostafa Rohaninejad, Pieter Abbeel.
arXiv 1712.09763

Safer Classification by Synthesis,
William Wang, Angelina Wang, Aviv Tamar, Xi Chen, Pieter Abbeel.
arXiv 1711.08534



[143] Variational Lossy Autoencoder,
Xi (Peter) Chen, Diederik P. Kingma, Tim Salimans, Yan (Rocky) Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, Pieter Abbeel.
In the proceedings of the International Conference on Learning Representations (ICLR), Toulon, France, April 2017. arXiv 1611.02731

[134] InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets,
Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, Pieter Abbeel.
In Neural Information Processing Systems (NIPS), Barcelona, Spain, December 2016. (arXiv 1606.03657)

[114] Deep Spatial Autoencoders for Visuomotor Learning
Chelsea Finn, Xin Yu Tan, Yan Duan, Trevor Darrell, Sergey Levine, Pieter Abbeel.
In the proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2016. (arXiv 1509.06113)