Update wording and links
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Antonin RAFFIN
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@ -44,6 +44,7 @@ Traceback (most recent call last): File ...
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**System Info**
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Describe the characteristic of your environment:
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* Describe how the library was installed (pip, docker, source, ...)
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* Stable-Baselines3 and sb3-contrib versions
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* GPU models and configuration
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* Python version
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* PyTorch version
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@ -1,4 +1,4 @@
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## Release 0.9.0a2 (WIP)
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## Release 0.10.0a0 (WIP)
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### Breaking Changes
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@ -2,7 +2,7 @@
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This contrib repository is designed for experimental implementations of various
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parts of reinforcement training so that others may make use of them. This includes full
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training algorithms, different tools (e.g. new environment wrappers,
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RL algorithms, different tools (e.g. new environment wrappers,
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callbacks) and extending algorithms implemented in stable-baselines3.
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**Before opening a pull request**, open an issue discussing the contribution.
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@ -10,9 +10,9 @@ Once we agree that the plan looks good, go ahead and implement it.
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Contributions and review focuses on following three parts:
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1) **Implementation quality**
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- Performance of the training algorithms should match what proposed authors reported (if applicable).
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- Performance of the RL algorithms should match the one reported by the original authors (if applicable).
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- This is ensured by including a code that replicates an experiment from the original
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paper or from an established codebase (e.g. the code from authors), as well as
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paper or from an established codebase (e.g. the code from authors), as well as
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a test to check that implementation works on program level (does not crash).
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2) Documentation
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- Documentation quality should match that of stable-baselines3, with each feature covered
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@ -20,7 +20,7 @@ Contributions and review focuses on following three parts:
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of logic and report of the expected results, where applicable.
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3) Consistency with stable-baselines3
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- To ease readability, all contributions need to follow the code style (see below) and
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idioms used in stable-baselines3.
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idioms used in stable-baselines3.
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The implementation quality is a strict requirements with little room for changes, because
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otherwise the implementation can do more harm than good (wrong results). Parts two and three
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@ -33,7 +33,7 @@ for suggestions of the community for new possible features to include in contrib
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## How to implement your suggestion
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Implement your feature/suggestion/algorithm in following ways, using the first one that applies:
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1) Environment wrapper: This can be used with any algorithm and even outside stable-baselines3.
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1) Environment wrapper: This can be used with any algorithm and even outside stable-baselines3.
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Place code for these under `sb3_contrib/common/wrappers` directory.
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2) [Custom callback](https://stable-baselines3.readthedocs.io/en/master/guide/callbacks.html).
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Place code under `sb3_contrib/common/callbacks` directory.
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@ -63,17 +63,17 @@ Along with the code, PR **must** include the following:
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this goes under respective pages in documentation. If full training algorithm, this goes under a new page with template below
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(`docs/modules/[algo_name]`).
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2) If a training algorithm/improvement: results of a replicated experiment from the original paper in the documentation,
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**which must match the results from authors** unless solid arguments can be provided why they did not match.
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**which must match the results from authors** unless solid arguments can be provided why they did not match.
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3) If above holds: The **exact** code to run the replicated experiment (i.e. it should produce the above results), and inside the
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code information about the environment used (Python version, library versions, OS, hardware information). If small enough,
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include this in the documentation. If applicable, use [rl-baselines3-zoo](https://github.com/DLR-RM/rl-baselines3-zoo) to
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run the agent performance comparison experiments (fork repository, implement experiment in a new branch and share link to
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run the agent performance comparison experiments (fork repository, implement experiment in a new branch and share link to
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that branch). If above do not apply, create new code to replicate the experiment and include link to it.
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4) Updated tests in `tests/test_run.py` and `tests/test_save_load.py` to test that features run as expected and serialize
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correctly. This this is **not** for testing e.g. training performance of a learning algorithm, and
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should be relatively quick to run.
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Below is a template for documentation for full training algorithms.
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Below is a template for documentation for full RL algorithms.
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```rst
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[Feature/Algorithm name]
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2
Makefile
2
Makefile
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@ -5,7 +5,7 @@ pytest:
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./scripts/run_tests.sh
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type:
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pytype
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pytype -j auto
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lint:
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# stop the build if there are Python syntax errors or undefined names
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37
README.md
37
README.md
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@ -1,31 +1,38 @@
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<img src="docs/\_static/img/logo.png" align="right" width="40%"/>
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[](https://github.com/Stable-Baselines-Team/stable-baselines3-contrib/actions) [](https://github.com/psf/black)
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# Stable-Baselines3 - Contrib
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# Stable-Baselines3 - Contrib (SB3-Contrib)
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Contrib package for [Stable-Baselines3](https://github.com/DLR-RM/stable-baselines3) - Experimental code.
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Contrib package for [Stable-Baselines3](https://github.com/DLR-RM/stable-baselines3) - Experimental reinforcement learning (RL) code.
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"sb3-contrib" for short.
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A place for training algorithms and tools that are considered experimental, e.g. implementations of the latest
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publications. Goal is to keep the simplicity, documentation and style of stable-baselines3 but for less matured
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implementations.
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### What is SB3-Contrib?
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Why create this repository? Over the span of stable-baselines and stable-baselines3, the community has been eager
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to contribute in form of better logging utilities, environment wrappers, extended support (e.g. different action spaces)
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and learning algorithms. However sometimes these utilities were too niche to be considered for stable-baselines or
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proved to be too difficult to integrate well into existing code without a mess. sb3-contrib aims to fix this by
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not requiring the neatest code integration with existing code and not setting limits on what is too niche: almost everything
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remotely useful goes! We hope this allows to extend the known quality of stable-baselines style and documentation beyond
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the relatively small scope of utilities of the main repository.
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A place for RL algorithms and tools that are considered experimental, e.g. implementations of the latest publications. Goal is to keep the simplicity, documentation and style of stable-baselines3 but for less matured implementations.
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### Why create this repository?
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Over the span of stable-baselines and stable-baselines3, the community has been eager to contribute in form of better logging utilities, environment wrappers, extended support (e.g. different action spaces) and learning algorithms.
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However sometimes these utilities were too niche to be considered for stable-baselines or
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proved to be too difficult to integrate well into existing code without a mess. sb3-contrib aims to fix this by not requiring the neatest code integration with existing code and not setting limits on what is too niche: almost everything remotely useful goes! We hope this allows to extend the known quality of stable-baselines style and documentation beyond the relatively small scope of utilities of the main repository.
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## Features
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See documentation for the full list of included features.
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**Training algorithms**:
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**RL Algorithms**:
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- [Truncated Quantile Critics (TQC)](https://arxiv.org/abs/2005.04269)
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<!-- TODO: uncomment when the repo is public -->
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<!-- ## Documentation
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Documentation is available online: [https://sb3-contrib.readthedocs.io/](https://sb3-contrib.readthedocs.io/) -->
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## Installation
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**Note:** You need the `master` version of [Stable Baselines3](https://github.com/DLR-RM/stable-baselines3/).
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@ -40,6 +47,10 @@ Install Stable Baselines3 - Contrib using pip:
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pip install git+https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
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```
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## How To Contribute
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If you want to contribute, please read [**CONTRIBUTING.md**](./CONTRIBUTING.md) guide first.
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## Citing the Project
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Welcome to Stable Baselines3 Contrib docs!
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==========================================
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Contrib package for `Stable Baselines3 <https://github.com/DLR-RM/stable-baselines3>`_ - Experimental code.
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Contrib package for `Stable Baselines3 (SB3) <https://github.com/DLR-RM/stable-baselines3>`_ - Experimental code.
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Github repository: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
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@ -64,7 +64,7 @@ To cite this project in publications:
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Contributing
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------------
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If you want to contribute, please read `CONTRIBUTING.md <https://github.com/DLR-RM/stable-baselines3/blob/master/CONTRIBUTING.md>`_ first.
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If you want to contribute, please read `CONTRIBUTING.md <https://github.com/Stable-Baselines-Team/stable-baselines3-contrib/blob/master/CONTRIBUTING.md>`_ first.
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Indices and tables
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-------------------
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