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awesome-model-based-reinforcement-learning

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A curated list of awesome Model-based reinforcement learning resources

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# Awesome Model-based Reinforcement Learning[![Awesome](https://awesome.re/badge.svg)](https://awesome.re)

A curated list of awesome Model-based Reinforcement Learning resources. Inspired by [awesome-deep-vision](https://github.com/kjw0612/awesome-deep-vision), [awesome-adversarial-machine-learning](https://github.com/yenchenlin/awesome-adversarial-machine-learning), [awesome-deep-learning-papers](https://github.com/terryum/awesome-deep-learning-papers), and [awesome-architecture-search](https://github.com/markdtw/awesome-architecture-search)

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

## Contributing
Please help contribute this list by adding [pull request](https://github.com/hjzh4/awesome-model-based-reinforcement-learning/pulls)

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

## Table of Contents
- [Thesis](#thesis)
- [Survey](#survey)
- [Conference Papers](#conference_papers)
- [Journal Papers](#journal_papers)  
- [Tutorials](#tutorials)
- [Tools](#tools)

## Thesis
- Efficient Reinforcement Learning using Gaussian Processes. [[pdf]](https://pdfs.semanticscholar.org/c9f2/1b84149991f4d547b3f0f625f710750ad8d9.pdf)
    - Marc Peter Deisenroth.
## Survey
#### 2017
- Survey of Model-Based Reinforcement Learning: Applications on Robotics. [[pdf]](https://link.springer.com/article/10.1007/s10846-017-0468-y)
    - Athanasios S. Polydoros and Lazaros Nalpantidis. *J Intell Robot Syst 2017*
## CV/CG
- Curiosity-driven Exploration by Self-supervised Prediction. [[pdf]](https://arxiv.org/pdf/1705.05363.pdf) [[code]](https://github.com/pathak22/noreward-rl)
    - 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]](http://www.roboticsproceedings.org/rss14/p44.pdf) [[code]](https://github.com/MarcToussaint/18-RSS-PhysicalManipulation)
    - Marc Toussaint, Kelsey R. Allen, Kevin A. Smith, Joshua B. Tenenbaum. *RSS 2018*
- A convex, smooth and invertible contact model for trajectory optimization. [[pdf]](https://homes.cs.washington.edu/~todorov/courses/amath533/ContactConvex.pdf)
    - Emanuel Todorov. *ICRA 2011*
- A Modular Differentiable Rigid Body Physics Engine. [[pdf]](https://drive.google.com/file/d/1K8t4gQExFXbuG4F9Zd2_30Y5wtpdEST7/view) [[code]](https://github.com/locuslab/lcp-physics)
    - Filipe de Avila Belbute-Peres, J. Zico Kolter. *Deep Reinforcement Learning Symposium, NIPS 2017*
- A DIFFERENTIABLE PHYSICS ENGINE FOR DEEP LEARNING IN ROBOTICS. [[pdf]](https://openreview.net/pdf?id=HkrB8XXte)
    - Jonas Degrave, Michiel Hermans, Joni Dambre, Francis wyffels. *ICLR 2017*
- Discovery of Complex Behaviors through Contact-Invariant Optimization. [[pdf]](https://homes.cs.washington.edu/~todorov/papers/MordatchSIGGRAPH12.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]](https://arxiv.org/abs/1708.02596) [[code]](https://github.com/nagaban2/nn_dynamics)
    - Anusha Nagabandi, Gregory Kahn, Ronald S. Fearing, Sergey Levine.
- Combining Model-Based and Model-Free Updates for Trajectory-Centric Reinforcement Learning. [[pdf]](https://arxiv.org/abs/1703.03078)
    - 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]](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4650850)
    - Duy Nguyen-Tuong and Jan Peters. *IROS 2008*
### Local model
- Learning Neural Network Policies with Guided Policy Search under Unknown Dynamics. [[pdf]](https://people.eecs.berkeley.edu/~svlevine/papers/mfcgps.pdf) [[code]](https://github.com/cbfinn/gps/blob/master/docs/index.md)
    - 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]](https://arxiv.org/pdf/1506.07365.pdf) [[code]](https://github.com/ericjang/e2c)
    - Manuel Watter, Jost Tobias Springenberg, Martin Riedmiller, Joschka Boedecker. *ICRA 2017*
- Deep Spatial Autoencoders for Visuomotor Learning. [[pdf]](https://arxiv.org/pdf/1509.06113.pdf) [[code]](https://github.com/cbfinn/gps/blob/master/docs/index.md)
    - 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]](https://papers.nips.cc/paper/6799-data-efficient-reinforcement-learning-in-continuous-state-action-gaussian-pomdps.pdf)
    - Rowan McAllister, Carl Rasmussen. *NIPS 2017*
- Improving PILCO with Bayesian Neural Network Dynamics Models. [[pdf]](http://mlg.eng.cam.ac.uk/yarin/PDFs/DeepPILCO.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]](http://mlg.eng.cam.ac.uk/pub/pdf/DeiRas11.pdf) [[code]](http://mlg.eng.cam.ac.uk/pilco/) [[unofficial code]](https://github.com/nrontsis/PILCO)
    - Marc Peter Deisenroth, Carl Rasmussen. *ICML 2011*
- Learning to Control a Low-Cost Manipulator using Data-Efficient Reinforcement Learning. [[pdf]](http://www.roboticsproceedings.org/rss07/p08.pdf)
    - Marc Peter Deisenroth, Carl Edward Rasmussen and Dieter Fox. *RSS 2011*
- Learning Dynamics Across Similar Spatiotemporally-Evolving Physical Systems. [[pdf]](http://proceedings.mlr.press/v78/whitman17a/whitman17a.pdf)
    - Joshua Whitman, Girish Chowdhary. *CoRL 2017*
## Journal Papers
## Tutorials
- Deep RL Bootcamp Lecture 9 Model-based Reinforcement Learning. Chelsea Finn (UC Berkeley) [[link]](https://www.youtube.com/watch?v=iC2a7M9voYU)
- Highlight Talk: Gaussian Processes for Data Efficient Learning. Marc Diesenroth [[link]](https://www.youtube.com/watch?v=dWsjjszwfi0)
## Tools
- [GPFlow](https://github.com/GPflow/GPflow)
- [GPy](https://github.com/SheffieldML/GPy)
## License
To the extent possible under law, we have waived all copyright and related or neighboring rights to this work.

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