bbrl- A Flexible and Simple Library for Reinforcement Learning deriving from SaLinA
BBRL stands for "BlackBoard Reinforcement Learning". Initially, this library was a fork of the SaLinA library. But SaLinA is a general model for sequential learning whereas BBRL is dedicated to RL, thus it focuses on a subset of SaLinA. Morevover, BBRL is designed for education purpose (in particular, to teach various RL algorithms, concepts and phenomena). Thus the fork slowly drifted away from SaLinA and became independent after a few months, even if some parts of the code are still inherited from SaLinA.
bbrl is a lightweight library extending PyTorch modules for developping Reinforcement Learning models
- It supports simultaneously training with AutoReset on multiple environments
- It works on multiple CPUs and GPUs
BBRL being inspired from SaLinA, please use this bibtex if you want to cite BBRL in your publications:
Link to the paper: SaLinA: Sequential Learning of Agents
@misc{salina,
author = {Ludovic Denoyer, Alfredo de la Fuente, Song Duong, Jean-Baptiste Gaya, Pierre-Alexandre Kamienny, Daniel H. Thompson},
title = {SaLinA: Sequential Learning of Agents},
year = {2021},
publisher = {Arxiv},
howpublished = {\url{https://github.com/facebookresearch/salina}},
}
- create and activate a python environment with your favorite tool, e.g. conda or venv (for instance,
conda create bbrl_env;conda activate bbrl_env) - Then clone the repo
pip install -e .
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April 2024:
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- Major evolution of the documentation
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March 2024:
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- Bug fixes in the replay buffer
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May-June 2023:
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- Integrated the use of gymnasium. Turned google colab notebooks into colab compatible jupyter notebooks. Refactored all the notebooks.
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August 2022:
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- Major updates of the notebook-based documentation
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May 2022:
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- First commit of the BBRL repository
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March 2022:
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- Forked SaLinA and started to modify the model
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BBRL only contains core classes to implement RL algorithms.
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Because both notations coexist in the literature, the GymAgent classes support the case where doing action
$a_t$ in state$s_t$ results in reward$r_t$ , and the case where it results in reward$r_{t+1}$ . -
Some output string were corrected, some variable were renamed and some comments were improved to favor code readability.
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A few small bugs in SaLinA were fixed:
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- The replay buffer was rejecting samples that did not fit inside when the number of added samples was beyond the limit. This has been corrected to implement the standard FIFO behavior of replay buffer.
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- When using autoreset=True and no replay buffer, transitions from an episode to the next were considered as standard steps in an episode. We added a mechanism to properly filter them out, using an additional
get_transitions()function in the Workspace class.
- When using autoreset=True and no replay buffer, transitions from an episode to the next were considered as standard steps in an episode. We added a mechanism to properly filter them out, using an additional
To help you understand how to use BBRL, we have written a doc here
If you want to learn RL on your own using BBRL, you can do so from the following online material
Most of the notebooks below can be run under jupyter notebook as well as under Google colaboratory. In any case, download it on your disk and run it with your favorite notebook environment:
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conda activate bbrl_env(for instance) -
jupyter notebook -
the notebook opens in your browser
Have a look at the bbrl_algos library.
See contributing
bbrl utilizes pytorch, hydra for configuring experiments, and gymnasium for reinforcement learning algorithms. See requirements.txt for more details.
bbrl is released under the MIT license. See LICENSE for additional details about it.