Update links in doc, fix broken links and move to https when possible

CITATION.bib

@@ -6,5 +6,5 @@ @article{stable-baselines3
   volume  = {22},
   number  = {268},
   pages   = {1-8},
-  url     = {http://jmlr.org/papers/v22/20-1364.html}
+  url     = {https://jmlr.org/papers/v22/20-1364.html}
 }

CONTRIBUTING.md

@@ -18,7 +18,7 @@ https://github.com/DLR-RM/stable-baselines3
 Note: If you do not follow the template (and its mandatory steps), your pull request will be ignored.
 
 If you are not familiar with creating a Pull Request, here are some guides:
-- http://stackoverflow.com/questions/14680711/how-to-do-a-github-pull-request
+- https://stackoverflow.com/questions/14680711/how-to-do-a-github-pull-request
 - https://help.github.com/articles/creating-a-pull-request/
 
 

NOTICE

@@ -2,7 +2,7 @@ Large portion of the code of Stable-Baselines3 (in `common/`) were ported from S
 both licensed under the MIT License:
 
 before the fork (June 2018):
-Copyright (c) 2017 OpenAI (http://openai.com)
+Copyright (c) 2017 OpenAI (https://openai.com)
 
 after the fork (June 2018):
 Copyright (c) 2018-2019 Stable-Baselines Team

README.md

@@ -256,15 +256,15 @@ To cite this repository in publications:
   volume  = {22},
   number  = {268},
   pages   = {1-8},
-  url     = {http://jmlr.org/papers/v22/20-1364.html}
+  url     = {https://jmlr.org/papers/v22/20-1364.html}
 }
 ```
 
 Note: If you need to refer to a specific version of SB3, you can also use the [Zenodo DOI](https://doi.org/10.5281/zenodo.8123988).
 
 ## Maintainers
 
-Stable-Baselines3 is currently maintained by [Ashley Hill](https://github.com/hill-a) (aka @hill-a), [Antonin Raffin](https://araffin.github.io/) (aka [@araffin](https://github.com/araffin)), [Maximilian Ernestus](https://github.com/ernestum) (aka @ernestum), [Adam Gleave](https://github.com/adamgleave) (@AdamGleave), [Anssi Kanervisto](https://github.com/Miffyli) (@Miffyli) and [Quentin Gallouédec](https://gallouedec.com/) (@qgallouedec).
+Stable-Baselines3 is currently maintained by [Ashley Hill](https://github.com/hill-a) (aka @hill-a), [Antonin Raffin](https://araffin.github.io/) (aka [@araffin](https://github.com/araffin)), [Maximilian Ernestus](https://github.com/ernestum) (aka @ernestum), [Adam Gleave](https://github.com/adamgleave) (@AdamGleave), [Anssi Kanervisto](https://github.com/Miffyli) (@Miffyli) and [Quentin Gallouédec](https://github.com/qgallouedec) (@qgallouedec).
 
 **Important Note: We do not provide technical support, or consulting** and do not answer personal questions via email.
 Please post your question on the [RL Discord](https://discord.com/invite/xhfNqQv), [Reddit](https://www.reddit.com/r/reinforcementlearning/), or [Stack Overflow](https://stackoverflow.com/) in that case.
@@ -279,7 +279,7 @@ If you want to contribute, please read [**CONTRIBUTING.md**](./CONTRIBUTING.md)
 
 The initial work to develop Stable Baselines3 was partially funded by the project *Reduced Complexity Models* from the *Helmholtz-Gemeinschaft Deutscher Forschungszentren*, and by the EU Horizon 2020 Research and Innovation Programme under grant number 951992 ([VeriDream](https://www.veridream.eu/)).
 
-The original version, Stable Baselines, was created in the [robotics lab U2IS](http://u2is.ensta-paristech.fr/index.php?lang=en) ([INRIA Flowers](https://flowers.inria.fr/) team) at [ENSTA ParisTech](http://www.ensta-paristech.fr/en).
+The original version, Stable Baselines, was created in the [robotics lab U2IS](http://u2is.ensta-paris.fr/?lang=en) ([INRIA Flowers](https://flowers.inria.fr/) team) at [ENSTA ParisTech](https://www.ensta.fr/en).
 
 
 Logo credits: [L.M. Tenkes](https://www.instagram.com/lucillehue/)

docs/conf.py

@@ -3,7 +3,7 @@
 #
 # This file does only contain a selection of the most common options. For a
 # full list see the documentation:
-# http://www.sphinx-doc.org/en/master/config
+# https://www.sphinx-doc.org/en/master/config
 
 # -- Path setup --------------------------------------------------------------
 
@@ -223,6 +223,6 @@ def setup(app):
 # Example configuration for intersphinx: refer to the Python standard library.
 # intersphinx_mapping = {
 #     'python': ('https://docs.python.org/3/', None),
-#     'numpy': ('http://docs.scipy.org/doc/numpy/', None),
-#     'torch': ('http://pytorch.org/docs/master/', None),
+#     'numpy': ('https://docs.scipy.org/doc/numpy/', None),
+#     'torch': ('https://pytorch.org/docs/master/', None),
 # }

docs/guide/checking_nan.md

@@ -152,4 +152,4 @@ As some datasets will sometimes fill missing values with NaNs as a surrogate val
 
 Here is some reading material about finding NaNs: <https://pandas.pydata.org/pandas-docs/stable/user_guide/missing_data.html>
 
-And filling the missing values with something else (imputation): <https://towardsdatascience.com/how-to-handle-missing-data-8646b18db0d4>
+And filling the missing values with something else (imputation): <https://towardsdatascience.com/missing-data-in-time-series-machine-learning-techniques-6b2273ff8b45/>

docs/guide/examples.md

@@ -448,7 +448,7 @@ model = PPO.load(log_dir / "ppo_halfcheetah", env=vec_env)
 
 ## Hindsight Experience Replay (HER)
 
-For this example, we use [Highway-Env](https://github.com/eleurent/highway-env) by [@eleurent](https://github.com/eleurent).
+For this example, we use [Highway-Env](https://github.com/Farama-Foundation/HighwayEnv) by [@eleurent](https://github.com/eleurent).
 
 ```{image} ../_static/img/colab-badge.svg
 :target: https://colab.research.google.com/github/Stable-Baselines-Team/rl-colab-notebooks/blob/sb3/stable_baselines_her.ipynb
@@ -639,7 +639,7 @@ Policies also offers a simple way to save/load weights as a NumPy vector, using
 and `load_from_vector()` method.
 
 Following example demonstrates reading parameters, modifying some of them and loading them to model
-by implementing [evolution strategy (es)](http://blog.otoro.net/2017/10/29/visual-evolution-strategies/)
+by implementing [evolution strategy (es)](https://blog.otoro.net/2017/10/29/visual-evolution-strategies/)
 for solving the `CartPole-v1` environment. The initial guess for parameters is obtained by running
 A2C policy gradient updates on the model.
 
@@ -722,7 +722,7 @@ Some massively parallel simulation environments such as [EnvPool](https://github
 To use SB3 with these tools, you need to wrap the environment with tool-specific `VecEnvWrapper` that preprocesses the data for SB3,
 you can find links to some of these wrappers in [issue #772](https://github.com/DLR-RM/stable-baselines3/issues/772#issuecomment-1048657002).
 
-- Isaac Lab wrapper: [link](https://github.com/isaac-sim/IsaacLab/blob/main/source/extensions/omni.isaac.lab_tasks/omni/isaac/lab_tasks/utils/wrappers/sb3.py)
+- Isaac Lab wrapper: [link](https://github.com/isaac-sim/IsaacLab/blob/3e73d6dd79080fd7632488c061052a6edd52e230/source/isaaclab_rl/isaaclab_rl/sb3.py#L93)
 - Brax: [link](https://gist.github.com/araffin/a7a576ec1453e74d9bb93120918ef7e7)
 - EnvPool: [link](https://github.com/sail-sg/envpool/blob/main/examples/sb3_examples/ppo.py)
 - Getting SAC to Work on a Massive Parallel Simulator: <https://araffin.github.io/post/sac-massive-sim/>

docs/guide/export.md

@@ -338,7 +338,7 @@ motivation for the code example above).
 
 The Coral chip is fast, with very low power consumption, but only has limited
 on-device training abilities. More information is on the webpage here:
-<https://coral.ai>.
+<https://developers.google.com/coral>.
 
 To deploy to a Coral, one must work via TFLite, and quantize the
 network to reflect the Coral's capabilities. The full chain to go from

docs/guide/imitation.md

@@ -13,5 +13,5 @@ imitation learning algorithms on top of Stable-Baselines3, including:
 
 You can install imitation with `pip install imitation`. The [imitation
 documentation](https://imitation.readthedocs.io/en/latest/) has more details
-on how to use the library, including [a quick start guide](https://imitation.readthedocs.io/en/latest/getting-started/first-steps.html)
+on how to use the library, including [a quick start guide](https://imitation.readthedocs.io/en/latest/getting-started/first_steps.html)
 for the impatient.

docs/guide/install.md

@@ -8,7 +8,7 @@ Stable-Baselines3 requires python 3.10+ and PyTorch >= 2.3
 
 ### Windows
 
-We recommend using [Anaconda](https://conda.io/docs/user-guide/install/windows.html) for Windows users for easier installation of Python packages and required libraries. You need an environment with Python version 3.8 or above.
+We recommend using [miniforge](https://github.com/conda-forge/miniforge#windows) for Windows users for easier installation of Python packages and required libraries. You need an environment with Python version 3.10 or above.
 
 For a quick start you can move straight to installing Stable-Baselines3 in the next step.
 
@@ -22,7 +22,7 @@ issue with atari-py package. [See this discussion for more information](https://
 To install Stable Baselines3 with pip, execute:
 
 ```bash
-pip install stable-baselines3[extra]
+pip install 'stable-baselines3[extra]'
 ```
 
 :::{note}
@@ -58,7 +58,7 @@ To contribute to Stable-Baselines3, with support for running tests and building
 
 ```bash
 git clone https://github.com/DLR-RM/stable-baselines3 && cd stable-baselines3
-pip install -e .[docs,tests,extra]
+pip install -e '.[docs,tests,extra]'
 ```
 
 ## Using Docker Images
@@ -101,7 +101,7 @@ Note: if you are using a proxy, you need to pass extra params during
 build and do some [tweaks]:
 
 ```bash
---network=host --build-arg HTTP_PROXY=http://your.proxy.fr:8080/ --build-arg http_proxy=http://your.proxy.fr:8080/ --build-arg HTTPS_PROXY=https://your.proxy.fr:8080/ --build-arg https_proxy=https://your.proxy.fr:8080/
+--network=host --build-arg HTTPS_PROXY=https://your.proxy.fr:8080/ --build-arg https_proxy=https://your.proxy.fr:8080/
 ```
 
 ### Run the images (CPU/GPU)

docs/guide/rl.md

@@ -13,6 +13,6 @@ However, if you want to learn about RL, there are several good resources to get
 - [RL103: From Deep Q-Learning (DQN) to Soft Actor-Critic (SAC) and Beyond](https://araffin.github.io/post/rl103/)
 - [Lilian Weng's blog](https://lilianweng.github.io/lil-log/2018/04/08/policy-gradient-algorithms.html)
 - [Berkeley's Deep RL Bootcamp](https://sites.google.com/view/deep-rl-bootcamp/lectures)
-- [Berkeley's Deep Reinforcement Learning course](http://rail.eecs.berkeley.edu/deeprlcourse/)
+- [Berkeley's Deep Reinforcement Learning course](https://rail.eecs.berkeley.edu/deeprlcourse/)
 - [Decisions & Dragons - FAQ for RL foundations](https://www.decisionsanddragons.com)
 - [More resources](https://github.com/dennybritz/reinforcement-learning)

docs/guide/sb3_contrib.md

@@ -35,7 +35,7 @@ See documentation for the full list of included features.
 - [Augmented Random Search (ARS)](https://arxiv.org/abs/1803.07055)
 - [Quantile Regression DQN (QR-DQN)]
 - [PPO with invalid action masking (Maskable PPO)](https://arxiv.org/abs/2006.14171)
-- [PPO with recurrent policy (RecurrentPPO aka PPO LSTM)](https://ppo-details.cleanrl.dev//2021/11/05/ppo-implementation-details/)
+- [PPO with recurrent policy (RecurrentPPO aka PPO LSTM)](https://iclr-blog-track.github.io/2022/03/25/ppo-implementation-details/)
 - [Truncated Quantile Critics (TQC)]
 - [Trust Region Policy Optimization (TRPO)](https://arxiv.org/abs/1502.05477)
 - [Batch Normalization in Deep Reinforcement Learning (CrossQ)](https://openreview.net/forum?id=PczQtTsTIX)

docs/guide/tensorboard.md

@@ -124,7 +124,7 @@ class ImageRecorderCallback(BaseCallback):
         image = self.training_env.render(mode="rgb_array")
         # "HWC" specify the dataformat of the image, here channel last
         # (H for height, W for width, C for channel)
-        # See https://pytorch.org/docs/stable/tensorboard.html
+        # See https://docs.pytorch.org/docs/stable/tensorboard.html
         # for supported formats
         self.logger.record("trajectory/image", Image(image, "HWC"), exclude=("stdout", "log", "json", "csv"))
         return True
@@ -223,7 +223,7 @@ class VideoRecorderCallback(BaseCallback):
                 """
                 # We expect `render()` to return a uint8 array with values in [0, 255] or a float array
                 # with values in [0, 1], as described in
-                # https://pytorch.org/docs/stable/tensorboard.html#torch.utils.tensorboard.writer.SummaryWriter.add_video
+                # https://docs.pytorch.org/docs/stable/tensorboard.html#torch.utils.tensorboard.writer.SummaryWriter.add_video
                 screen = self._eval_env.render(mode="rgb_array")
                 # PyTorch uses CxHxW vs HxWxC gym (and tensorflow) image convention
                 screens.append(screen.transpose(2, 0, 1))
@@ -297,7 +297,7 @@ model.learn(total_timesteps=int(5e4), callback=HParamCallback())
 
 ## Directly Accessing The Summary Writer
 
-If you would like to log arbitrary data (in one of the formats supported by [pytorch](https://pytorch.org/docs/stable/tensorboard.html)), you
+If you would like to log arbitrary data (in one of the formats supported by [PyTorch](https://docs.pytorch.org/docs/stable/tensorboard.html)), you
 can get direct access to the underlying SummaryWriter in a callback:
 
 :::{warning}
@@ -306,7 +306,7 @@ This is method is not recommended and should only be used by advanced users.
 
 :::{note}
 If you want a concrete example, you can watch [how to log lap time with donkeycar env](https://www.youtube.com/watch?v=v8j2bpcE4Rg&t=4619s),
-or read the code in the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo/blob/feat/gym-donkeycar/rl_zoo3/callbacks.py#L251-L270).
+or read the code in the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo/blob/eb5d9c7770abe9a60f5511193ebcb260dfdc2706/rl_zoo3/callbacks.py#L262).
 You might also want to take a look at [issue #1160](https://github.com/DLR-RM/stable-baselines3/issues/1160) and [issue #1219](https://github.com/DLR-RM/stable-baselines3/issues/1219).
 :::