The project is under active development
Baconian: Boosting the model-based reinforcement learning
Baconian [beˈkonin] is a toolbox for model-based reinforcement learning with user-friendly experiment setting-up, logging and visualization modules developed by CAP. We aim to develop a flexible, re-usable and modularized framework that can allow the user's to easily set-up a model-based rl experiments by reuse modules we offered.
Release news:
- 2019.4.26 We just finished our demo paper to introduce Baconian: https://arxiv.org/abs/1904.10762
- 2019.4.4 Released the v0.1.4. Added benchmark results on DDPG, visualization of results will be given later. Fixed some bugs.
- 2019.3.24 Released the v0.1.3. Added GP dynamics, fix some bugs.
- 2019.3.23 Released the v0.1.2. Added linear dynamics, iLQR, LQR methods.
For previous news, please go here
Documentation
Documentation is available at http://baconian-public.readthedocs.io/
TODO and Road Map
Currently, the project is under activate development. We are working towards a stable 1.0 version. Details of the road map and future plans will be released as soon as possible.
Currently we are working on
- Visualization module
- Simplify flow module
- State-of-art model-based algorithms: PILCO, GPS etc.
- Latent-space method supporting.
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
- PILCO: https://github.com/nrontsis/PILCO
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 during your usage of the package, please open an issue or send an email to me ([email protected]) with detailed information. I appreciate your help!