https://github.com/facebookresearch/rlmeta
RLMeta is a light-weight flexible framework for Distributed Reinforcement Learning Research.
https://github.com/facebookresearch/rlmeta
Last synced: 3 months ago
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RLMeta is a light-weight flexible framework for Distributed Reinforcement Learning Research.
- Host: GitHub
- URL: https://github.com/facebookresearch/rlmeta
- Owner: facebookresearch
- License: mit
- Archived: true
- Created: 2021-12-16T02:47:46.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2023-02-11T06:20:06.000Z (about 2 years ago)
- Last Synced: 2024-08-13T13:34:06.682Z (6 months ago)
- Language: Python
- Size: 381 KB
- Stars: 285
- Watchers: 15
- Forks: 28
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Citation: CITATION.cff
Awesome Lists containing this project
- awesome-production-machine-learning - RLMeta - RLMeta is a flexible lightweight research framework for Distributed Reinforcement Learning based on PyTorch and moolib (Industry Strength RL)
README
# RLMeta
rlmeta - a flexible lightweight research framework for Distributed
Reinforcement Learning based on [`PyTorch`](https://pytorch.org/) and
[`moolib`](https://github.com/facebookresearch/moolib)
## Installation
To build from source, please install [`PyTorch`](https://pytorch.org/) first,
and then run the commands below.
```
$ git clone https://github.com/facebookresearch/rlmeta
$ cd rlmeta
$ git submodule sync && git submodule update --init --recursive
$ pip install -e .
```
## Run an Example
To run the example for Atari Pong game with PPO algorithm:
```
$ cd examples/atari/ppo
$ python atari_ppo.py env.game="Pong" num_epochs=20
```
We are using [`hydra`](https://hydra.cc/) to define configs for trainining jobs.
The configs are defined in
```
./conf/conf_ppo.yaml
```
The logs and checkpoints will be automatically saved to
```
./outputs/{YYYY-mm-dd}/{HH:MM:SS}/
```
After training, we can draw the training curve by run
```
$ python ../../plot.py --log_file=./outputs/{YYYY-mm-dd}/{HH:MM:SS}/atari_ppo.log --fig_file=./atari_ppo.png --xkey=time
```
One example of the training curve is shown below.
## License
rlmeta is licensed under the MIT License. See [`LICENSE`](LICENSE) for details.