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Baconian [beˈkonin] is a toolbox for model-based reinforcement learning with user-friendly experiment setting-up, loggingand visualization modules developed byCAP. We aim to develop a flexible, re-usable andmodularized framework that can allow the users to easily set-up their model-based RL experiments by reusing modules weprovide.

You can easily install by (with python 3.5/3.6/3.7, Ubuntu 16.04/18.04):

# install tensorflow with/without GPU based on your machinepip install tensorflow-gpu==1.15.2# orpip install tensorflow==1.15.2 pip install baconian

For more advance usage like using Mujoco environment, please refer to our documentation page.

• 2020.04.29 v0.2.2 Fix some memory issues in SampleData module, and simplify some APIs.
• 2020.02.10 We are including external reward & terminal function of Gym/mujoco tasks with well-written documents.
• 2020.01.30 Update some dependent packages versions, and release some preliminary benchmark results with hyper-parameters.

For previous news, please gohere

We support python 3.5, 3.6, and 3.7 with Ubuntu 16.04 or 18.04.Documentation is available athttp://baconian-public.readthedocs.io/

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Kurutach, Thanard, et al. "Model-ensemble trust-region policy optimization." arXiv preprint arXiv:1802.10592 (2018).

Mnih, Volodymyr, et al. "Playing atari with deep reinforcement learning." arXiv preprint arXiv:1312.5602 (2013).

Schulman, John, et al. "Proximal policy optimization algorithms." arXiv preprint arXiv:1707.06347 (2017).

Lillicrap, Timothy P., et al. "Continuous control with deep reinforcement learning." arXiv preprint arXiv:1509.02971 (2015).

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Nagabandi, Anusha, et al. "Neural network dynamics for model-based deep reinforcement learning with model-free fine-tuning." 2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2018.

Levine, Sergey, et al. "End-to-end training of deep visuomotor policies." The Journal of Machine Learning Research 17.1 (2016): 1334-1373.

Thanks to the following open-source projects:

• garage:https://github.com/rlworkgroup/garage
• rllab:https://github.com/rll/rllab
• baselines:https://github.com/openai/baselines
• gym:https://github.com/openai/gym
• trpo:https://github.com/pat-coady/trpo

If you find Baconian is useful for your research, please consider cite our demo paper here:

@article{linsen2019baconian, title={Baconian: A Unified Opensource Framework for Model-Based Reinforcement Learning}, author={Linsen, Dong and Guanyu, Gao and Yuanlong, Li and Yonggang, Wen}, journal={arXiv preprint arXiv:1904.10762},year={2019} }

If you find any bugs on issues, please open an issue or send an email to me(linsen001@e.ntu.edu.sg) with detailed information. I appreciate your help!