Metadata-Version: 2.1
Name: fastrl
Version: 0.0.46
Summary: fastrl is a reinforcement learning library that extends Fastai. This project is not affiliated with fastai or Jeremy Howard.
Home-page: https://github.com/josiahls/fastrl/tree/main/
Author: Josiah Laivins, and contributors
Author-email: 
License: Apache Software License 2.0
Keywords: fastrl reinforcement learning rl robotics fastai deep learning machine learning
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Provides-Extra: dev
License-File: LICENSE

fastrl
================

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> Warning: 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 “terminateds” 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

Below will install the alpha build of fastrl.

**Cuda Install**

`pip install fastrl==0.0.* --pre --extra-index-url https://download.pytorch.org/whl/nightly/cu113`

**Cpu Install**

`pip install fastrl==0.0.* --pre --extra-index-url https://download.pytorch.org/whl/nightly/cpu`

## Docker (highly recommend)

Install:
[Nvidia-Docker](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker)

Install: [docker-compose](https://docs.docker.com/compose/install/)

``` bash
docker-compose pull && docker-compose up
```

## Contributing

After you clone this repository, please run `nbdev_install_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_clean` 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`.
