Multi Task RL Algorithms
MTRL is a library of multi-task reinforcement learning algorithms. It has two main components:
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Building blocks and agents that implement the multi-task RL algorithms.
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Experiment setups that enable training/evaluation on different setups.
Together, these two components enable use of MTRL across different environments and setups.
List of publications & submissions using MTRL (please create a pull request to add the missing entries):
- Learning Robust State Abstractions for Hidden-Parameter Block MDPs
- Multi-Task Reinforcement Learning with Context-based Representations
- We use the
af8417bfc82a3e249b4b02156518d775f29eb289commit for the MetaWorld environments for our experiments.
- We use the
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MTRL uses MIT License.
If you use MTRL in your research, please use the following BibTeX entry:
@Misc{Sodhani2021MTRL,
author = {Shagun Sodhani and Amy Zhang},
title = {MTRL - Multi Task RL Algorithms},
howpublished = {Github},
year = {2021},
url = {https://github.com/facebookresearch/mtrl}
}
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Clone the repository:
git clone git@github.com:facebookresearch/mtrl.git. -
Install dependencies:
pip install -r requirements/dev.txt
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MTRL supports 8 different multi-task RL algorithms as described here.
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MTRL supports multi-task environments using MTEnv. These environments include MetaWorld and multi-task variants of DMControl Suite
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Refer the tutorial to get started with MTRL.
There are several ways to contribute to MTRL.
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Use MTRL in your research.
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Contribute a new algorithm. We currently support 8 multi-task RL algorithms and are looking forward to adding more environments.
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Check out the good-first-issues on GitHub and contribute to fixing those issues.
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Check out additional details here.
Ask questions in the chat or github issues: