Package: baconian
Baconian:  Boosting model-based reinforcement learningBaconian [beˈkonin] is a toolbox for model-based reinforcement learning with user-friendly experiment setting-up, logging
and visualization modules developed byCAP. We aim to develop a flexible, re-usable and
modularized framework that can allow the users to easily set-up their model-based RL experiments by reusing modules we
provide.InstallationYou 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 machinepipinstalltensorflow-gpu==1.15.2# orpipinstalltensorflow==1.15.2pipinstallbaconianFor more advance usage like using Mujoco environment, please refer to our documentation page.Release news: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 gohereDocumentationWe support python 3.5, 3.6, and 3.7 with Ubuntu 16.04 or 18.04.
Documentation is available athttp://baconian-public.readthedocs.io/Algorithms Reference:Model-based:1. DynaSutton, Richard S. "Dyna, an integrated architecture for learning, planning, and reacting." ACM Sigart Bulletin 2.4 (1991): 160-163.2. LQRAbbeel, P. "Optimal Control for Linear Dynamical Systems and Quadratic Cost (‘LQR’)." (2012).3. iLQRAbbeel, P. "Optimal Control for Linear Dynamical Systems and Quadratic Cost (‘LQR’)." (2012).4. MPCGarcia, Carlos E., David M. Prett, and Manfred Morari. "Model predictive control: theory and practice—a survey." Automatica 25.3 (1989): 335-348.5. Model-ensembleKurutach, Thanard, et al. "Model-ensemble trust-region policy optimization." arXiv preprint arXiv:1802.10592 (2018).Model-free1. DQNMnih, Volodymyr, et al. "Playing atari with deep reinforcement learning." arXiv preprint arXiv:1312.5602 (2013).2. PPOSchulman, John, et al. "Proximal policy optimization algorithms." arXiv preprint arXiv:1707.06347 (2017).3. DDPGLillicrap, Timothy P., et al. "Continuous control with deep reinforcement learning." arXiv preprint arXiv:1509.02971 (2015).Algorithms in Progress1. Random ShootingRao, Anil V. "A survey of numerical methods for optimal control." Advances in the Astronautical Sciences 135.1 (2009): 497-528.2. MB-MFNagabandi, 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, 2018.3. GPSLevine, Sergey, et al. "End-to-end training of deep visuomotor policies." The Journal of Machine Learning Research 17.1 (2016): 1334-1373.AcknowledgementThanks to the following open-source projects:garage:https://github.com/rlworkgroup/garagerllab:https://github.com/rll/rllabbaselines:https://github.com/openai/baselinesgym:https://github.com/openai/gymtrpo:https://github.com/pat-coady/trpoCiting BaconianIf 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} 
}Report an issueIf 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!