Awesome Model-based Reinforcement Learning
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!
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Markdown format:
- 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
2017
- Survey of Model-Based Reinforcement Learning: Applications on Robotics. [pdf]
- Athanasios S. Polydoros and Lazaros Nalpantidis. J Intell Robot Syst 2017
CV/CG
- Curiosity-driven Exploration by Self-supervised Prediction. [pdf] [code]
- Deepak Pathak, Pulkit Agrawal, Alexei A. Efros, 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, 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, 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, Francis wyffels. ICLR 2017
- Discovery of Complex Behaviors through Contact-Invariant Optimization. [pdf]
- Igor Mordatch, Emanuel Tordorov, 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, 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, Sergey Levine. ICML 2017
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, Joschka Boedecker. ICRA 2017
- Deep Spatial Autoencoders for Visuomotor Learning. [pdf] [code]
- Chelsea Finn, Xin Yu Tan, Yan Duan, Trevor Darrell, Sergey Levine, Pieter Abbeel. ICRA 2016
Gaussian Process
- Data-Efficient Reinforcement Learning in Continuous-State POMDPs. [pdf]
- Rowan McAllister, 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]
- Marc Peter Deisenroth, 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, Girish Chowdhary. CoRL 2017
Journal Papers
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]
Tools
License
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