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Update readme and clarify planned features (DLR-RM#2030)
* Update readme and clarify planned features
* Fix rtd python version
* Fix pip version for rtd
* Update rtd ubuntu and mambaforge
* Add upper bound for gymnasium
* [ci skip] Update readme
@@ -22,6 +22,8 @@ These algorithms will make it easier for the research community and industry to
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**The performance of each algorithm was tested** (see *Results* section in their respective page),
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you can take a look at the issues [#48](https://github.com/DLR-RM/stable-baselines3/issues/48) and [#49](https://github.com/DLR-RM/stable-baselines3/issues/49) for more details.
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We also provide detailed logs and reports on the [OpenRL Benchmark](https://wandb.ai/openrlbenchmark/sb3) platform.
@@ -41,7 +43,13 @@ you can take a look at the issues [#48](https://github.com/DLR-RM/stable-baselin
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### Planned features
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Please take a look at the [Roadmap](https://github.com/DLR-RM/stable-baselines3/issues/1) and [Milestones](https://github.com/DLR-RM/stable-baselines3/milestones).
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Since most of the features from the [original roadmap](https://github.com/DLR-RM/stable-baselines3/issues/1) have been implemented, there are no major changes planned for SB3, it is now *stable*.
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If you want to contribute, you can search in the issues for the ones where [help is welcomed](https://github.com/DLR-RM/stable-baselines3/labels/help%20wanted) and the other [proposed enhancements](https://github.com/DLR-RM/stable-baselines3/labels/enhancement).
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While SB3 development is now focused on bug fixes and maintenance (doc update, user experience, ...), there is more active development going on in the associated repositories:
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- newer algorithms are regularly added to the [SB3 Contrib](https://github.com/Stable-Baselines-Team/stable-baselines3-contrib) repository
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- faster variants are developed in the [SBX (SB3 + Jax)](https://github.com/araffin/sbx) repository
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- the training framework for SB3, the RL Zoo, has an active [roadmap](https://github.com/DLR-RM/rl-baselines3-zoo/issues/299)
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## Migration guide: from Stable-Baselines (SB2) to Stable-Baselines3 (SB3)
We implement experimental features in a separate contrib repository: [SB3-Contrib](https://github.com/Stable-Baselines-Team/stable-baselines3-contrib)
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This allows SB3 to maintain a stable and compact core, while still providing the latest features, like Recurrent PPO (PPO LSTM), Truncated Quantile Critics (TQC), Quantile Regression DQN (QR-DQN) or PPO with invalid action masking (Maskable PPO).
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This allows SB3 to maintain a stable and compact core, while still providing the latest features, like Recurrent PPO (PPO LSTM), CrossQ, Truncated Quantile Critics (TQC), Quantile Regression DQN (QR-DQN) or PPO with invalid action masking (Maskable PPO).
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Documentation is available online: [https://sb3-contrib.readthedocs.io/](https://sb3-contrib.readthedocs.io/)
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@@ -97,17 +105,16 @@ It provides a minimal number of features compared to SB3 but can be much faster
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### Prerequisites
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Stable Baselines3 requires Python 3.8+.
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#### Windows 10
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#### Windows
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To install stable-baselines on Windows, please look at the [documentation](https://stable-baselines3.readthedocs.io/en/master/guide/install.html#prerequisites).
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### Install using pip
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Install the Stable Baselines3 package:
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```sh
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pip install 'stable-baselines3[extra]'
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```
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pip install stable-baselines3[extra]
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```
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**Note:** Some shells such as Zsh require quotation marks around brackets, i.e. `pip install 'stable-baselines3[extra]'` ([More Info](https://stackoverflow.com/a/30539963)).
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This includes an optional dependencies like Tensorboard, OpenCV or `ale-py` to train on atari games. If you do not need those, you can use:
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```sh
@@ -177,6 +184,7 @@ All the following examples can be executed online using Google Colab notebooks:
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