Metadata-Version: 2.1
Name: baconian
Version: 0.2.6
Summary: model-based reinforcement learning toolbox
Home-page: https://github.com/cap-ntu/baconian-project
Author: Linsen Dong
Author-email: linsen001@e.ntu.edu.sg
License: MIT License
Description: # Baconian:  Boosting model-based reinforcement learning 
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        Baconian [beˈkonin] is a toolbox for model-based reinforcement learning with user-friendly experiment setting-up, logging 
        and visualization modules developed by [CAP](http://cap.scse.ntu.edu.sg/). 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.
        ### Installation 
        
        You can easily install by (with python 3.5/3.6/3.7, Ubuntu 16.04/18.04): 
        ```bash
        # install tensorflow with/without GPU based on your machine
        pip install tensorflow-gpu==1.15.2
        # or
        pip install tensorflow==1.15.2 
        
        pip install baconian
        ```
        
        For 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 go [here](./old_news.md) 
        
        ### Documentation
        We support python 3.5, 3.6, and 3.7 with Ubuntu 16.04 or 18.04.
        Documentation is available at http://baconian-public.readthedocs.io/
        
        ### Algorithms Reference:
        
        #### Model-based: 
        
        #### 1. Dyna
        Sutton, Richard S. "Dyna, an integrated architecture for learning, planning, and reacting." ACM Sigart Bulletin 2.4 (1991): 160-163.
        #### 2. LQR
        Abbeel, P. "Optimal Control for Linear Dynamical Systems and Quadratic Cost (‘LQR’)." (2012).
        #### 3. iLQR
        Abbeel, P. "Optimal Control for Linear Dynamical Systems and Quadratic Cost (‘LQR’)." (2012).
        #### 4. MPC
        Garcia, Carlos E., David M. Prett, and Manfred Morari. "Model predictive control: theory and practice—a survey." Automatica 25.3 (1989): 335-348.
        #### 5. Model-ensemble
        Kurutach, Thanard, et al. "Model-ensemble trust-region policy optimization." arXiv preprint arXiv:1802.10592 (2018).
        
        #### Model-free
        
        #### 1. DQN
        Mnih, Volodymyr, et al. "Playing atari with deep reinforcement learning." arXiv preprint arXiv:1312.5602 (2013).
        #### 2. PPO
        Schulman, John, et al. "Proximal policy optimization algorithms." arXiv preprint arXiv:1707.06347 (2017).
        #### 3. DDPG
        Lillicrap, Timothy P., et al. "Continuous control with deep reinforcement learning." arXiv preprint arXiv:1509.02971 (2015).
        
        ### Algorithms in Progress
        #### 1. Random Shooting
        Rao, Anil V. "A survey of numerical methods for optimal control." Advances in the Astronautical Sciences 135.1 (2009): 497-528.
        #### 2. MB-MF
        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.
        #### 3. GPS
        Levine, Sergey, et al. "End-to-end training of deep visuomotor policies." The Journal of Machine Learning Research 17.1 (2016): 1334-1373.
        
        ### Acknowledgement 
        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
        
        ### Citing Baconian
        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} 
        }
        ```
        ### Report an issue 
        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!
        
Platform: UNKNOWN
Requires-Python: >=3.5
Description-Content-Type: text/markdown
