https://github.com/haoliuhl/taming-maml
Taming MAML: efficient unbiased meta-reinforcement learning
https://github.com/haoliuhl/taming-maml
meta-learning reinfor
Last synced: over 1 year ago
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Taming MAML: efficient unbiased meta-reinforcement learning
- Host: GitHub
- URL: https://github.com/haoliuhl/taming-maml
- Owner: haoliuhl
- Created: 2019-05-02T18:56:11.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2022-09-30T19:47:10.000Z (almost 4 years ago)
- Last Synced: 2025-02-28T21:37:02.762Z (over 1 year ago)
- Topics: meta-learning, reinfor
- Language: Python
- Size: 194 KB
- Stars: 29
- Watchers: 1
- Forks: 3
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
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README
# Taming MAML: Efficient unbiased meta-reinforcement learning
Reference Tensorflow implementation of [Taming MAML: Efficient unbiased meta-reinforcement learning](http://proceedings.mlr.press/v97/liu19g.html).
We will release Pytorch version later.
## Getting started
You can use [Dockerfile](Dockerfile) to build an image with conda environment called _tmaml_ included, activating this conda env:
```
conda activate tmaml
```
you can also use [`tmaml.yml`](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`](tmaml_run_mujoco.py) , [`vpg_run_mujoco.py`](vpg_run_mujoco.py) and [`dice_vpg_run_mujoco.py`](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
- [x] Adding TMAML
- [x] Adding MAML
- [x] Adding DICE
- [ ] Benchmarking
- [ ] Pytorch version
### Acknowledgements
This repository is based on [ProMP repo](https://github.com/jonasrothfuss/ProMP).