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fastrl2-2.0.9
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fastrl is a reinforcement learning library that extends Fastai. This project is not affiliated with fastai or Jeremy Howard.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | fastrl2-2.0.9 |
| نام | fastrl2 |
| نسخه کتابخانه | 2.0.9 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Josiah Laivins, and contributors |
| ایمیل نویسنده | - |
| آدرس صفحه اصلی | https://github.com/josiahls/fastrl2/tree/master/ |
| آدرس اینترنتی | https://pypi.org/project/fastrl2/ |
| مجوز | Apache Software License 2.0 |
# Fastrl2
> This is a temporary location for fastrl version 2.
{% include warning.html content='Even before fastrl==2.0.0, all Models should converge reasonably fast, however HRL models `DADS` and `DIAYN` will need ' %}re-balancing and some extra features that the respective authors used.
# Overview
Here is change
Fastai for computer vision and tabular learning has been amazing. One would wish that this would be the same for RL. The purpose of this repo is to have a framework that is as easy as possible to start, but also designed for testing new agents.
Documentation is being served at https://josiahls.github.io/fastrl/ from documentation directly generated via `nbdev` in this repo.
# Current Issues of Interest
## Data Issues
- [ ] data and async_data are still buggy. We need to verify that the order that the data being returned is the best it can be for our models. We need to make sure that "dones" are returned and that there are new duplicate (unless intended)
- [ ] Better data debugging. Do environments skips steps correctly? Do n_steps work correct?
# Whats new?
As we have learned how to support as many RL agents as possible, we found that `fastrl==1.*` was vastly limited in the models that it can support. `fastrl==2.*` will leverage the `nbdev` library for better documentation and more relevant testing. We also will be building on the work of the `ptan`<sup>1</sup> library as a close reference for pytorch based reinforcement learning APIs.
<sup>1</sup> "Shmuma/Ptan". Github, 2020, https://github.com/Shmuma/ptan. Accessed 13 June 2020.
## Install
## PyPI (Not implemented yet)
Placeholder here, there is no pypi package yet. It is recommended to do traditional forking.
(For future, currently there is no pypi persion)`pip install fastrl==2.0.0 --pre`
## Conda
`conda env create -f environment.yaml`
`source activate fastrl && pip install ptan --no-dependencies && python setup.py develop`
## Docker (highly recommend)
For cpu execution
```bash
docker build -f fastrl.Dockerfile -t fastrl:latest .
docker run --rm -it -p 8888:8888 --user "$(id -u):$(id -g)" -v $(pwd):/home/fastrl/fastrl fastrl:latest /bin/bash
#docker run --rm -it -p 8888:8888 -p 4000:4000 --user "$(id -u):$(id -g)" -v $(pwd):/opt/project/fastrl fastrl:latest /bin/bash
```
Install: [Nvidia-Docker](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker)
```bash
docker build -f fastrl_cuda.Dockerfile -t fastrl_cuda:latest .
docker run --rm -it -p 8888:8888 -p 4000:4000 --gpus all --user "$(id -u):$(id -g)" -v $(pwd):/opt/project/fastrl fastrl_cuda:latest /bin/bash
```
## Contributing
After you clone this repository, please run `nbdev_install_git_hooks` in your terminal. This sets up git hooks, which clean up the notebooks to remove the extraneous stuff stored in the notebooks (e.g. which cells you ran) which causes unnecessary merge conflicts.
Before submitting a PR, check that the local library and notebooks match. The script `nbdev_diff_nbs` can let you know if there is a difference between the local library and the notebooks.
* If you made a change to the notebooks in one of the exported cells, you can export it to the library with `nbdev_build_lib` or `make fastai2`.
* If you made a change to the library, you can export it back to the notebooks with `nbdev_update_lib`.
نیازمندی
| مقدار | نام |
|---|---|
| >=2.0.0 | fastai |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6 | Python |
نحوه نصب
نصب پکیج whl fastrl2-2.0.9:
pip install fastrl2-2.0.9.whl
نصب پکیج tar.gz fastrl2-2.0.9:
pip install fastrl2-2.0.9.tar.gz