Pieter Abbeel

Reinforcement Learning with Augmented Data

Michael Laskin, Kimin Lee, Adam Stooke, Lerrel Pinto, Pieter Abbeel, Aravind Srinivas

Comments NeurIPS 2020 camera-ready version. First two authors contributed equally, website: https://mishalaskin.github.io/rad code: https://github.com/MishaLaskin/rad and https://github.com/pokaxpoka/rad_procgen

Guided Meta-Policy Search

Russell Mendonca, Abhishek Gupta, Rosen Kralev, Pieter Abbeel, Sergey Levine, Chelsea Finn

Comments Published at Neurips 2019. Website : https://sites.google.com/berkeley.edu/guided-metapolicy-search

Trajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement Learning

Younggyo Seo, Kimin Lee, Ignasi Clavera, Thanard Kurutach, Jinwoo Shin, Pieter Abbeel

Comments Accepted in NeurIPS2020. First two authors contributed equally, website: https://sites.google.com/view/trajectory-mcl code: https://github.com/younggyoseo/trajectory_mcl

Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model

Alex X. Lee, Anusha Nagabandi, Pieter Abbeel, Sergey Levine

Comments Project website: https://alexlee-gk.github.io/slac/

LaND: Learning to Navigate from Disengagements

Gregory Kahn, Pieter Abbeel, Sergey Levine

Inverse Reward Design

Dylan Hadfield-Menell, Smitha Milli, Pieter Abbeel, Stuart Russell, Anca Dragan

Comments Advances in Neural Information Processing Systems 30 (NIPS 2017) Revised Oct 2020 to fix a typo in Eq. 3

Robust Reinforcement Learning using Adversarial Populations

Eugene Vinitsky, Yuqing Du, Kanaad Parvate, Kathy Jang, Pieter Abbeel, Alexandre Bayen

CURL: Contrastive Unsupervised Representations for Reinforcement Learning

Aravind Srinivas, Michael Laskin, Pieter Abbeel

Comments First two authors contributed equally, website: https://mishalaskin.github.io/curl code: https://github.com/MishaLaskin/curl

Hierarchically Decoupled Imitation for Morphological Transfer

Donald J. Hejna, Pieter Abbeel, Lerrel Pinto

Comments International Conference on Machine Learning (ICML) 2020 camera ready submission

Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow

Xue Bin Peng, Angjoo Kanazawa, Sam Toyer, Pieter Abbeel, Sergey Levine

Visual Imitation Made Easy

Sarah Young, Dhiraj Gandhi, Shubham Tulsiani, Abhinav Gupta, Pieter Abbeel, Lerrel Pinto

Hybrid Discriminative-Generative Training via Contrastive Learning

Hao Liu, Pieter Abbeel

Comments Code: https://github.com/lhao499/HDGE

Dynamics Generalization via Information Bottleneck in Deep Reinforcement Learning

Xingyu Lu, Kimin Lee, Pieter Abbeel, Stas Tiomkin

Comments 16 pages

Preventing Imitation Learning with Adversarial Policy Ensembles

Albert Zhan, Stas Tiomkin, Pieter Abbeel

Predictive Coding for Boosting Deep Reinforcement Learning with Sparse Rewards

Xingyu Lu, Stas Tiomkin, Pieter Abbeel

Learning Efficient Representation for Intrinsic Motivation

Ruihan Zhao, Stas Tiomkin, Pieter Abbeel

Plan Arithmetic: Compositional Plan Vectors for Multi-Task Control

Coline Devin, Daniel Geng, Pieter Abbeel, Trevor Darrell, Sergey Levine

Comments In NeurIPS 2019

Variable Skipping for Autoregressive Range Density Estimation

Eric Liang, Zongheng Yang, Ion Stoica, Pieter Abbeel, Yan Duan, Xi Chen

Comments ICML 2020. Code released at: https://var-skip.github.io/

Responsive Safety in Reinforcement Learning by PID Lagrangian Methods

Adam Stooke, Joshua Achiam, Pieter Abbeel

Comments ICML 2020

Planning to Explore via Self-Supervised World Models

Ramanan Sekar, Oleh Rybkin, Kostas Daniilidis, Pieter Abbeel, Danijar Hafner, Deepak Pathak

Comments Accepted at ICML 2020. Videos and code at https://ramanans1.github.io/plan2explore/

Locally Masked Convolution for Autoregressive Models

Ajay Jain, Pieter Abbeel, Deepak Pathak

Comments Published at Conference on Uncertainty in AI (UAI) 2020

Adaptive Online Planning for Continual Lifelong Learning

Kevin Lu, Igor Mordatch, Pieter Abbeel

Comments Originally published in NeurIPS Deep RL 2019

AVID: Learning Multi-Stage Tasks via Pixel-Level Translation of Human Videos

Laura Smith, Nikita Dhawan, Marvin Zhang, Pieter Abbeel, Sergey Levine

Comments Robotics: Science and Systems (RSS) 2020 camera ready submission. Project website: https://sites.google.com/view/rss20avid

Automatic Curriculum Learning through Value Disagreement

Yunzhi Zhang, Pieter Abbeel, Lerrel Pinto

Comments https://sites.google.com/berkeley.edu/vds/

Mutual Information-based State-Control for Intrinsically Motivated Reinforcement Learning

Rui Zhao, Yang Gao, Pieter Abbeel, Volker Tresp, Wei Xu

Comments 15 pages, 8 figures

Goal-conditioned Imitation Learning

Yiming Ding, Carlos Florensa, Mariano Phielipp, Pieter Abbeel

Comments Published at NeurIPS 2019

Mutual Information Maximization for Robust Plannable Representations

Yiming Ding, Ignasi Clavera, Pieter Abbeel

Comments Accepted at NeurIPS 2019 Workshop on Robot Learning: Control and Interaction in the Real World

Model-Augmented Actor-Critic: Backpropagating through Paths

Ignasi Clavera, Violet Fu, Pieter Abbeel

Comments Accepted paper at ICLR 2020

Checkmate: Breaking the Memory Wall with Optimal Tensor Rematerialization

Paras Jain, Ajay Jain, Aniruddha Nrusimha, Amir Gholami, Pieter Abbeel, Kurt Keutzer, Ion Stoica, Joseph E. Gonzalez

Comments In Proceedings of 3rd Conference Machine Learning and Systems 2020 (MLSys 2020)

Sub-policy Adaptation for Hierarchical Reinforcement Learning

Alexander C. Li, Carlos Florensa, Ignasi Clavera, Pieter Abbeel

Comments ICLR 2020