Taming MAML: Efficient unbiased meta-reinforcement learning
Reference Tensorflow implementation of Taming MAML: Efficient unbiased meta-reinforcement learning. We will release Pytorch version later.
Getting started
You can use Dockerfile to build an image with conda environment called tmaml included, activating this conda env:
conda activate tmaml
you can also use tmaml.yml to create a conda env called tmaml.
conda env create -f tmaml.yml
then activate this conda env
conda activate tmaml
Usage
You can use the tmaml_run_mujoco.py , vpg_run_mujoco.py and dice_vpg_run_mujoco.py scripts in order to run reinforcement learning experiments with different algorithm.
MAML:
python vpg_run_mujoco.py --env HalfCheetahRandDirecEnv
MAML + DICE:
python dice_vpg_run_mujoco.py --env HalfCheetahRandDirecEnv
TMAML:
python tmaml_run_mujoco.py --env HalfCheetahRandDirecEnv
References
To cite TMAML please use
@InProceedings{pmlr-v97-liu19g,
title = {Taming {MAML}: Efficient unbiased meta-reinforcement learning},
author = {Liu, Hao and Socher, Richard and Xiong, Caiming},
booktitle = {Proceedings of the 36th International Conference on Machine Learning},
pages = {4061--4071},
year = {2019},
editor = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
volume = {97},
series = {Proceedings of Machine Learning Research},
address = {Long Beach, California, USA},
month = {09--15 Jun},
publisher = {PMLR},
}
TODOs
- Adding TMAML
- Adding MAML
- Adding DICE
- Benchmarking
- Pytorch version
Acknowledgements
This repository is based on ProMP repo.
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