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This repository contains the codebase for our accepted paper in the Main track of AAMAS'24, titled 'Policy Learning for Off-Dynamics RL with Deficient Support'.

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Policy Learning for Off-Dynamics RL with Deficient Support

This is the source code for replicating the results from our paper accepted to AAMAS'24, titled 'Policy Learning for Off-Dynamics RL with Deficient Support'.

Thank you for your interest!

Setup

Before training, please install the following packages and libraries by running the following command

conda create --name dads
conda activate dads
pip install -r requirements.txt

Training

Implementation for envs is in the defficient_support_mujoco_noise_envs.py and DADS in dads.py Run the command below to train the DADS agents

python dads.py --device=cuda

Citation

If you find our code helpful or utilise our proposed method as comparison baselines in your experiments, please cite our paper. Again, thank you for your interest!

@inproceedings{10.5555/3635637.3662965,
author = {Le Pham Van, Linh and The Tran, Hung and Gupta, Sunil},
title = {Policy Learning for Off-Dynamics RL with Deficient Support},
year = {2024},
isbn = {9798400704864},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
booktitle = {Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems},
pages = {1093–1100},
series = {AAMAS '24}
}

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This repository contains the codebase for our accepted paper in the Main track of AAMAS'24, titled 'Policy Learning for Off-Dynamics RL with Deficient Support'.

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