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A curated list of awesome Model-based reinforcement learning resources

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Awesome Model-based Reinforcement LearningAwesome

A curated list of awesome Model-based Reinforcement Learning resources. Inspired by awesome-deep-vision, awesome-adversarial-machine-learning, awesome-deep-learning-papers, and awesome-architecture-search

Model-based Reinforcement Learning is gaining popularity in Robotics community. These are some of the awesome resources!

Contributing

This repo is forked from https://github.com/Lukeeeeee/awesome-model-based-reinforcement-learning, and I will continue to maintain it by myself.

Markdown format for conference/journal papers:

- Paper Name [[pdf]](link) [[code]](link)
  - Author 1, Author 2 and Author 3. *Conference/Journal Year*

Table of Contents

Thesis

  • Efficient Reinforcement Learning using Gaussian Processes. [pdf]
    • Marc Peter Deisenroth.

Survey

  • Survey of Model-Based Reinforcement Learning: Applications on Robotics. [pdf]
    • Athanasios S. Polydoros and Lazaros Nalpantidis. J Intell Robot Syst 2017
  • A Survey on Policy Search for Robotics. [pdf]
    • Marc Peter Deisenroth, Gerhard Neumann and Jan Peters.

CV/CG

  • Curiosity-driven Exploration by Self-supervised Prediction. [pdf] [code]
    • Deepak Pathak, Pulkit Agrawal, Alexei A. Efros and Trevor Darrell. ICML 2017

Conference papers

Physics Model

  • Differentiable Physics and Stable Modes for Tool-Use and Manipulation Planning. [pdf] [code]
    • Marc Toussaint, Kelsey R. Allen, Kevin A. Smith and Joshua B. Tenenbaum. RSS 2018
  • A convex, smooth and invertible contact model for trajectory optimization. [pdf]
    • Emanuel Todorov. ICRA 2011
  • A Modular Differentiable Rigid Body Physics Engine. [pdf] [code]
    • Filipe de Avila Belbute-Peres and J. Zico Kolter. Deep Reinforcement Learning Symposium, NIPS 2017
  • A DIFFERENTIABLE PHYSICS ENGINE FOR DEEP LEARNING IN ROBOTICS. [pdf]
    • Jonas Degrave, Michiel Hermans, Joni Dambre and Francis wyffels. ICLR 2017
  • Discovery of Complex Behaviors through Contact-Invariant Optimization. [pdf]
    • Igor Mordatch, Emanuel Tordorov and Zoran Popovic. TOG'12

Hybrid model-based and model-free algorithm

  • Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning. [pdf] [code]
    • Anusha Nagabandi, Gregory Kahn, Ronald S. Fearing and Sergey Levine.
  • Combining Model-Based and Model-Free Updates for Trajectory-Centric Reinforcement Learning. [pdf]
    • Yevgen Chebotar, Karol Hausman, Marvin Zhang, Gaurav Sukhatme, Stefan Schaal and Sergey Levine. ICML 2017
  • Combined Reinforcement Learning via Abstract Representations. [pdf] [code]
    • Vincent François-Lavet, Yoshua Bengio, Doina Precup and Joelle Pineau. AAAI 2019
  • When to Trust Your Model: Model-Based Policy Optimization. [pdf] [code] [project page]
    • Michael Janner, Justin Fu, Marvin Zhang and Sergey Levine. NeurIPS 2019

Model Predictive Control

  • Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models. [pdf] [code] [project page]
    • Kurtland Chua, Roberto Calandra, Rowan McAllister and Sergey Levine. NIPS 2018

Optimal Control

  • Local Gaussian Process Regression for Real-time Model-based Robot Control. [pdf]
    • Duy Nguyen-Tuong and Jan Peters. IROS 2008

Local model

  • Learning Neural Network Policies with Guided Policy Search under Unknown Dynamics. [pdf] [code]
    • Sergey Levine and Pieter Abbeel. NIPS 2014

Learn in latent space

Foward Dynamics Model

  • Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images. [pdf] [code]
    • Manuel Watter, Jost Tobias Springenberg, Martin Riedmiller and Joschka Boedecker. ICRA 2017
  • Deep Spatial Autoencoders for Visuomotor Learning. [pdf] [code]
    • Chelsea Finn, Xin Yu Tan, Yan Duan, Trevor Darrell, Sergey Levine and Pieter Abbeel. ICRA 2016

Gaussian Process

  • Data-Efficient Reinforcement Learning in Continuous-State POMDPs. [pdf]
    • Rowan McAllister and Carl Rasmussen. NIPS 2017
  • Improving PILCO with Bayesian Neural Network Dynamics Models. [pdf]
    • Yarin Gal and Rowan Thomas McAllister and Carl Edward Rasmussen. Data-Efficient Machine Learning workshop, ICML, 2016
  • PILCO: A Model-Based and Data-Efficient Approach to Policy Search. [pdf] [code] [unofficial code] [unofficial code 2]
    • Marc Peter Deisenroth and Carl Rasmussen. ICML 2011
  • Learning to Control a Low-Cost Manipulator using Data-Efficient Reinforcement Learning. [pdf]
    • Marc Peter Deisenroth, Carl Edward Rasmussen and Dieter Fox. RSS 2011
  • Learning Dynamics Across Similar Spatiotemporally-Evolving Physical Systems. [pdf]
    • Joshua Whitman and Girish Chowdhary. CoRL 2017

Journal papers

ArXiv only or under review papers

  • MOPO: Model-based Offline Policy Optimization. [pdf]
    • Tianhe Yu, Garrett Thomas, Lantao Yu, Stefano Ermon, James Zou, Sergey Levine, Chelsea Finn and Tengyu Ma.
  • Model-based Reinforcement Learning: A Survey. [pdf]
    • Thomas M. Moerland, Joost Broekens and Catholijn M. Jonker.

Tutorials

  • Deep RL Bootcamp Lecture 9 Model-based Reinforcement Learning. Chelsea Finn (UC Berkeley) [link]
  • Highlight Talk: Gaussian Processes for Data Efficient Learning. Marc Diesenroth [link]
  • CS287-FA19 Advanced Robotics: Lecture 20 Model-Based Reinforcement Learning. Pieter Abbeel (UC Berkeley) [link 1] [link 2]
  • Reinforcement Learning 7: Planning and Models. Hado Van Hasselt (Deepmind) [link]

Tools

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A curated list of awesome Model-based reinforcement learning resources