Table of Contents


Researchers

People Department
Timothy Lillicrap DeepMind
Oriol Vinyals DeepMind
Jessica Hamrick DeepMind
Shixiang Gu Google Brain
Danijar Hafner Google Brain
Vitchyr H. Pong UC Berkeley
Anusha Nagabandi UC Berkeley
Ignasi Clavera UC Berkeley
Thanard Kurutach UC Berkeley
Tingwu Wang University of Toronto

Blog

DeepMind

UC Berkeley

Jonathan Hui

天津包子馅儿

Others

Conference & Workshop

Library

Meta-Policy Search’s documentation, including

  • ProMP: Proximal Meta-Policy Search (Rothfuss et al., 2018)
  • MAML: Model Agnostic Meta-Learning (Finn et al., 2017)
  • E-MAML: Exploration MAML (Al-Shedivat et al., 2018, Stadie et al., 2018)

Papers

2019 - Motion Planning Networks: Bridging the Gap Between Learning-based and Classical Motion Planners

  • Ahmed H. Qureshi, Yinglong Miao, Anthony Simeonov, Michael C. Yip
  • University of California San Diego
  • [Project Home] [Paper]

2019 - Variational Inference MPC forBayesian Model-based Reinforcement Learning

  • Masashi Okada, Tadahiro Taniguchi
  • Ritsumeikan Univ. & Panasonic Corp
  • [Paper]

2019 - A Model-based Approach for Sample-efficient Multi-task Reinforcement Learning

  • Nicholas C. Landolfi, Garrett Thomas, Tengyu Ma
  • Stanford
  • [Paper]

2019 - Dynamics-Aware Unsupervised Discovery of Skills

  • Archit Sharma, Shixiang Gu, Sergey Levine, Vikash Kumar, Karol Hausman
  • Google Brain
  • [Project Home]

2019 - Benchmarking Model-Based Reinforcement Learning

  • Tingwu Wang, Xuchan Bao, Ignasi Clavera, Jerrick Hoang, Yeming Wen, Eric Langlois, Shunshi Zhang, Guodong Zhang, Pieter Abbeel, Jimmy Ba
  • University of Toronto & UC Berkeley & Vector Institute
  • [Project Home] [Github] [Paper]

2019 - Baconian: A Unified Opensource Framework for Model-Based Reinforcement Learning

ICML’19 - Calibrated Model-Based Deep Reinforcement Learning

  • Ali Malik, Volodymyr Kuleshov, Jiaming Song, Danny Nemer, Harlan Seymour, Stefano Ermon
  • Stanford
  • [Github] [Slide] [Paper]

ICML’19 Workshop - When to Trust Your Model: Model-Based Policy Optimization

2019 - Exploring Model-based Planning with Policy Networks

ICLR’19 - Model-Predictive Policy Learning with Uncertainty Regularization for Driving in Dense Traffic

ICLR’19 - (SLBO) Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees

  • Yuping Luo, Huazhe Xu, Yuanzhi Li, Yuandong Tian, Trevor Darrell, Tengyu Ma
  • Princeton, UC Berkeley, Facebook, Stanford
  • [Github] [Poster] [Paper]

ICLR’19 - Learning to Adapt in Dynamic, Real-World Environment through Meta-Reinforcement Learning

ICLR’19 - Deep Online Learning via Meta-Learning: Continual Adaptation for Model-Based RL

ICLR’19 - ProMP: Proximal Meta-Policy Search

NIPS’18 - (STEVE) Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion

ICLR’18 - (ME-TRPO) Model-Ensemble Trust-Region Policy Optimization

CORL’18 - (MB-MPO) Model-Based Reinforcement Learning via Meta-Policy Optimization