https://github.com/geyang/e-maml

E-MAML, and RL-MAML baseline implemented in Tensorflow v1
https://github.com/geyang/e-maml

Last synced: about 2 months ago
JSON representation

E-MAML, and RL-MAML baseline implemented in Tensorflow v1

• Host: GitHub
• URL: https://github.com/geyang/e-maml
• Owner: geyang
• Created: 2019-12-07T20:30:28.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2019-12-07T22:14:05.000Z (about 5 years ago)
• Last Synced: 2024-04-14T15:11:50.619Z (9 months ago)
• Language: Python
• Homepage:
• Size: 54.7 KB
• Stars: 15
• Watchers: 0
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# E-MAML Implementation

This repo contains the full implementation of the E-MAML algorithm from the paper

[*Some Considerations on Learning to Explore via Meta-Reinforcement Learning*][NIPS_link]

[NIPS_link]: https://papers.nips.cc/paper/8140-the-importance-of-sampling-inmeta-reinforcement-learning

## Structure of This Codebase

The main implementation is contained in the `e_maml_tf` directory. Inside the `e_maml_experiments` 

directory we provide a light weight half-cheetah baseline for verification. The original

KrazyWorld codebase is not opensourced. So we implemented a new KrazyWorld environment. To run E-MAML

on this new KrazyWorld, you need to add a thin adaptor following the convention in `custom_vendor`

and `sampler.py`.

:point_right: [`KrazyWorld` github repo][KrazyWorld]

[KrazyWorld]: https://github.com/bstadie/krazyworld.git

# Getting Started:

1. Setup conda environment with python 3.6.4 or above. (this is required for all of the `f-string` literals.)

2. if on mac, run `brew install mpich`. this is the MPI version that `baseline` and `mpi4py` relies on.

3. run `pip install -e .`. If the `mpi4py` installation fails, try `pip install mpi4py` in a new terminal session.

4. if `mujoco-py` complains (which fails the installation), make sure you have installed mujoco and have a working license key.

5. If not, you should download mujoco for your environment and place the license key `mjkey.txt` under `~/.mujoco/`.

6. Distributed Setup: Add a file `.yours` inside `e_maml_experiments` that contains the following content:

    ```yaml

    username: 

    project: e_maml

    logging_server: http://:8081

    ```

   

   If you are not using a distributed logging setup, you can leave the logging_server to `none` or 

   leave it empty. In that case it would be logged to you `~/ml-logger-outputs` directory.

   

# Cite

To cite E-MAML please use

```bibtex

@article{stadie2018e-maml,

  title={Some considerations on learning to explore via meta-reinforcement learning},

  author={Stadie, Bradly C and Yang, Ge and Houthooft, Rein and Chen, Xi and Duan, Yan and Wu, Yuhuai and Abbeel, Pieter and Sutskever, Ilya},

  journal={arXiv preprint arXiv:1803.01118},

  year={2018}

}

```