[release_15] Release 15 update versions (#5101)

* Update versions

* Fix for validate release links

* Update release tag and docs

README.md

 
 # Unity ML-Agents Toolkit
 
-[![docs badge](https://img.shields.io/badge/docs-reference-blue.svg)](https://github.com/Unity-Technologies/ml-agents/tree/release_14_docs/docs/)
+[![docs badge](https://img.shields.io/badge/docs-reference-blue.svg)](https://github.com/Unity-Technologies/ml-agents/tree/release_15_docs/docs/)
 
 [![license badge](https://img.shields.io/badge/license-Apache--2.0-green.svg)](LICENSE)
 
 
 
 **Our latest, stable release is `Release 14`. Click
-[here](https://github.com/Unity-Technologies/ml-agents/tree/release_14_docs/docs/Readme.md)
+[here](https://github.com/Unity-Technologies/ml-agents/tree/release_15_docs/docs/Readme.md)
 to get started with the latest release of ML-Agents.**
 
 The table below lists all our releases, including our `main` branch which is

com.unity.ml-agents.extensions/Documentation~/Grid-Sensor.md

 
 An image can be thought of as a matrix of a predefined width (W) and a height (H) and each pixel can be thought of as simply an array of length 3 (in the case of RGB), `[Red, Green, Blue]` holding the different channel information of the color (channel) intensities at that pixel location. Thus an image is just a 3 dimensional matrix of size WxHx3. A Grid Observation can be thought of as a generalization of this setup where in place of a pixel there is a "cell" which is an array of length N representing different channel intensities at that cell position. From a Convolutional Neural Network point of view, the introduction of multiple channels in an "image" isn't a new concept. One such example is using an RGB-Depth image which is used in several robotics applications. The distinction of Grid Observations is what the data within the channels represents. Instead of limiting the channels to color intensities, the channels within a cell of a Grid Observation generalize to any data that can be represented by a single number (float or int).
 
-Before jumping into the details of the Grid Sensor, an important thing to note is the agent performance and qualitatively different behavior over raycasts. Unity MLAgent's comes with a suite of example environments. One in particular, the [Food Collector](https://github.com/Unity-Technologies/ml-agents/tree/release_14_docs/docs/Learning-Environment-Examples.md#food-collector), has been the focus of the Grid Sensor development.
+Before jumping into the details of the Grid Sensor, an important thing to note is the agent performance and qualitatively different behavior over raycasts. Unity MLAgent's comes with a suite of example environments. One in particular, the [Food Collector](https://github.com/Unity-Technologies/ml-agents/tree/release_15_docs/docs/Learning-Environment-Examples.md#food-collector), has been the focus of the Grid Sensor development.
 
 The Food Collector environment can be described as:
 * Set-up: A multi-agent environment where agents compete to collect food.

com.unity.ml-agents.extensions/Documentation~/Match3.md

 This implementation includes:
 
 * C# implementation catered toward a Match-3 setup including concepts around encoding for moves based on [Human Like Playtesting with Deep Learning](https://www.researchgate.net/publication/328307928_Human-Like_Playtesting_with_Deep_Learning)
-* An example Match-3 scene with ML-Agents implemented (located under /Project/Assets/ML-Agents/Examples/Match3). More information, on Match-3 example [here](https://github.com/Unity-Technologies/ml-agents/tree/release_14_docs/docs/docs/Learning-Environment-Examples.md#match-3).
+* An example Match-3 scene with ML-Agents implemented (located under /Project/Assets/ML-Agents/Examples/Match3). More information, on Match-3 example [here](https://github.com/Unity-Technologies/ml-agents/tree/release_15_docs/docs/docs/Learning-Environment-Examples.md#match-3).
 
 ### Feedback
 If you are a Match-3 developer and are trying to leverage ML-Agents for this scenario, [we want to hear from you](https://forms.gle/TBsB9jc8WshgzViU9). Additionally, we are also looking for interested Match-3 teams to speak with us for 45 minutes. If you are interested, please indicate that in the [form](https://forms.gle/TBsB9jc8WshgzViU9).  If selected, we will provide gift cards as a token of appreciation.

com.unity.ml-agents.extensions/Documentation~/com.unity.ml-agents.extensions.md

 recommended ways to install the package:
 
 ### Local Installation
-[Clone the repository](https://github.com/Unity-Technologies/ml-agents/tree/release_14_docs/docs/Installation.md#clone-the-ml-agents-toolkit-repository-optional) and follow the
-[Local Installation for Development](https://github.com/Unity-Technologies/ml-agents/tree/release_14_docs/docs/Installation.md#advanced-local-installation-for-development-1)
+[Clone the repository](https://github.com/Unity-Technologies/ml-agents/tree/release_15_docs/docs/Installation.md#clone-the-ml-agents-toolkit-repository-optional) and follow the
+[Local Installation for Development](https://github.com/Unity-Technologies/ml-agents/tree/release_15_docs/docs/Installation.md#advanced-local-installation-for-development-1)
-![Package Manager git URL](https://github.com/Unity-Technologies/ml-agents/blob/release_14_docs/docs/images/unity_package_manager_git_url.png)
+![Package Manager git URL](https://github.com/Unity-Technologies/ml-agents/blob/release_15_docs/docs/images/unity_package_manager_git_url.png)
-git+https://github.com/Unity-Technologies/ml-agents.git?path=com.unity.ml-agents.extensions#release_14
+git+https://github.com/Unity-Technologies/ml-agents.git?path=com.unity.ml-agents.extensions#release_15
-"com.unity.ml-agents.extensions": "git+https://github.com/Unity-Technologies/ml-agents.git?path=com.unity.ml-agents.extensions#release_14",
+"com.unity.ml-agents.extensions": "git+https://github.com/Unity-Technologies/ml-agents.git?path=com.unity.ml-agents.extensions#release_15",
 ```
 See [Git dependencies](https://docs.unity3d.com/Manual/upm-git.html#subfolder) for more information. Note that this
 may take several minutes to resolve the packages the first time that you add it.
     - No way to customize the action space of the `InputActuatorComponent`
 
 ## Need Help?
-The main [README](https://github.com/Unity-Technologies/ml-agents/tree/release_14_docs/README.md) contains links for contacting the team or getting support.
+The main [README](https://github.com/Unity-Technologies/ml-agents/tree/release_15_docs/README.md) contains links for contacting the team or getting support.

com.unity.ml-agents.extensions/package.json

 {
   "name": "com.unity.ml-agents.extensions",
   "displayName": "ML Agents Extensions",
-  "version": "0.2.0-preview",
+  "version": "0.3.0-preview",
-    "com.unity.ml-agents": "1.8.1-preview"
+    "com.unity.ml-agents": "1.9.0-preview"
   }
 }

com.unity.ml-agents/Documentation~/com.unity.ml-agents.md

 [unity ML-Agents Toolkit]: https://github.com/Unity-Technologies/ml-agents
 [unity inference engine]: https://docs.unity3d.com/Packages/com.unity.barracuda@latest/index.html
 [package manager documentation]: https://docs.unity3d.com/Manual/upm-ui-install.html
-[installation instructions]: https://github.com/Unity-Technologies/ml-agents/blob/release_14_docs/docs/Installation.md
+[installation instructions]: https://github.com/Unity-Technologies/ml-agents/blob/release_15_docs/docs/Installation.md
-[ML-Agents GitHub repo]: https://github.com/Unity-Technologies/ml-agents/blob/release_14_docs/com.unity.ml-agents.extensions
+[ML-Agents GitHub repo]: https://github.com/Unity-Technologies/ml-agents/blob/release_15_docs/com.unity.ml-agents.extensions

com.unity.ml-agents/Runtime/Academy.cs

  * API. For more information on each of these entities, in addition to how to
  * set-up a learning environment and train the behavior of characters in a
  * Unity scene, please browse our documentation pages on GitHub:
- * https://github.com/Unity-Technologies/ml-agents/tree/release_14_docs/docs/
+ * https://github.com/Unity-Technologies/ml-agents/tree/release_15_docs/docs/
  */
 
 namespace Unity.MLAgents
     /// fall back to inference or heuristic decisions. (You can also set agents to always use
     /// inference or heuristics.)
     /// </remarks>
-    [HelpURL("https://github.com/Unity-Technologies/ml-agents/tree/release_14_docs/" +
+    [HelpURL("https://github.com/Unity-Technologies/ml-agents/tree/release_15_docs/" +
         "docs/Learning-Environment-Design.md")]
     public class Academy : IDisposable
     {
         /// Unity package version of com.unity.ml-agents.
         /// This must match the version string in package.json and is checked in a unit test.
         /// </summary>
-        internal const string k_PackageVersion = "1.8.1-preview";
+        internal const string k_PackageVersion = "1.9.0-preview";
 
         const int k_EditorTrainingPort = 5004;
 

com.unity.ml-agents/Runtime/Actuators/IActionReceiver.cs

         ///
         /// See [Agents - Actions] for more information on masking actions.
         ///
-        /// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_14_docs/docs/Learning-Environment-Design-Agents.md#actions
+        /// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_15_docs/docs/Learning-Environment-Design-Agents.md#actions
         /// </remarks>
         /// <seealso cref="IActionReceiver.OnActionReceived"/>
         void WriteDiscreteActionMask(IDiscreteActionMask actionMask);

com.unity.ml-agents/Runtime/Actuators/IDiscreteActionMask.cs

         ///
         /// See [Agents - Actions] for more information on masking actions.
         ///
-        /// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_14_docs/docs/Learning-Environment-Design-Agents.md#actions
+        /// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_15_docs/docs/Learning-Environment-Design-Agents.md#actions
         /// </remarks>
         /// <param name="branch">The branch for which the actions will be masked.</param>
         /// <param name="actionIndices">The indices of the masked actions.</param>

com.unity.ml-agents/Runtime/Agent.cs

     /// [OnDisable()]: https://docs.unity3d.com/ScriptReference/MonoBehaviour.OnDisable.html]
     /// [OnBeforeSerialize()]: https://docs.unity3d.com/ScriptReference/MonoBehaviour.OnBeforeSerialize.html
     /// [OnAfterSerialize()]: https://docs.unity3d.com/ScriptReference/MonoBehaviour.OnAfterSerialize.html
-    /// [Agents]: https://github.com/Unity-Technologies/ml-agents/blob/release_14_docs/docs/Learning-Environment-Design-Agents.md
-    /// [Reinforcement Learning in Unity]: https://github.com/Unity-Technologies/ml-agents/blob/release_14_docs/docs/Learning-Environment-Design.md
+    /// [Agents]: https://github.com/Unity-Technologies/ml-agents/blob/release_15_docs/docs/Learning-Environment-Design-Agents.md
+    /// [Reinforcement Learning in Unity]: https://github.com/Unity-Technologies/ml-agents/blob/release_15_docs/docs/Learning-Environment-Design.md
-    /// [Unity ML-Agents Toolkit manual]: https://github.com/Unity-Technologies/ml-agents/blob/release_14_docs/docs/Readme.md
+    /// [Unity ML-Agents Toolkit manual]: https://github.com/Unity-Technologies/ml-agents/blob/release_15_docs/docs/Readme.md
-    [HelpURL("https://github.com/Unity-Technologies/ml-agents/blob/release_14_docs/" +
+    [HelpURL("https://github.com/Unity-Technologies/ml-agents/blob/release_15_docs/" +
         "docs/Learning-Environment-Design-Agents.md")]
     [Serializable]
     [RequireComponent(typeof(BehaviorParameters))]
         /// for information about mixing reward signals from curiosity and Generative Adversarial
         /// Imitation Learning (GAIL) with rewards supplied through this method.
         ///
-        /// [Agents - Rewards]: https://github.com/Unity-Technologies/ml-agents/blob/release_14_docs/docs/Learning-Environment-Design-Agents.md#rewards
-        /// [Reward Signals]: https://github.com/Unity-Technologies/ml-agents/blob/release_14_docs/docs/ML-Agents-Overview.md#a-quick-note-on-reward-signals
+        /// [Agents - Rewards]: https://github.com/Unity-Technologies/ml-agents/blob/release_15_docs/docs/Learning-Environment-Design-Agents.md#rewards
+        /// [Reward Signals]: https://github.com/Unity-Technologies/ml-agents/blob/release_15_docs/docs/ML-Agents-Overview.md#a-quick-note-on-reward-signals
         /// </remarks>
         /// <param name="reward">The new value of the reward.</param>
         public void SetReward(float reward)
         /// for information about mixing reward signals from curiosity and Generative Adversarial
         /// Imitation Learning (GAIL) with rewards supplied through this method.
         ///
-        /// [Agents - Rewards]: https://github.com/Unity-Technologies/ml-agents/blob/release_14_docs/docs/Learning-Environment-Design-Agents.md#rewards
-        /// [Reward Signals]: https://github.com/Unity-Technologies/ml-agents/blob/release_14_docs/docs/ML-Agents-Overview.md#a-quick-note-on-reward-signals
+        /// [Agents - Rewards]: https://github.com/Unity-Technologies/ml-agents/blob/release_15_docs/docs/Learning-Environment-Design-Agents.md#rewards
+        /// [Reward Signals]: https://github.com/Unity-Technologies/ml-agents/blob/release_15_docs/docs/ML-Agents-Overview.md#a-quick-note-on-reward-signals
         ///</remarks>
         /// <param name="increment">Incremental reward value.</param>
         public void AddReward(float increment)
         /// implementing a simple heuristic function can aid in debugging agent actions and interactions
         /// with its environment.
         ///
-        /// [Demonstration Recorder]: https://github.com/Unity-Technologies/ml-agents/blob/release_14_docs/docs/Learning-Environment-Design-Agents.md#recording-demonstrations
-        /// [Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_14_docs/docs/Learning-Environment-Design-Agents.md#actions
+        /// [Demonstration Recorder]: https://github.com/Unity-Technologies/ml-agents/blob/release_15_docs/docs/Learning-Environment-Design-Agents.md#recording-demonstrations
+        /// [Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_15_docs/docs/Learning-Environment-Design-Agents.md#actions
         /// [GameObject]: https://docs.unity3d.com/Manual/GameObjects.html
         /// </remarks>
         /// <example>
         /// For more information about observations, see [Observations and Sensors].
         ///
         /// [GameObject]: https://docs.unity3d.com/Manual/GameObjects.html
-        /// [Observations and Sensors]: https://github.com/Unity-Technologies/ml-agents/blob/release_14_docs/docs/Learning-Environment-Design-Agents.md#observations-and-sensors
+        /// [Observations and Sensors]: https://github.com/Unity-Technologies/ml-agents/blob/release_15_docs/docs/Learning-Environment-Design-Agents.md#observations-and-sensors
         /// </remarks>
         public virtual void CollectObservations(VectorSensor sensor)
         {
         ///
         /// See [Agents - Actions] for more information on masking actions.
         ///
-        /// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_14_docs/docs/Learning-Environment-Design-Agents.md#actions
+        /// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_15_docs/docs/Learning-Environment-Design-Agents.md#actions
         /// </remarks>
         /// <seealso cref="IActionReceiver.OnActionReceived"/>
         public virtual void WriteDiscreteActionMask(IDiscreteActionMask actionMask)
         ///
         /// For more information about implementing agent actions see [Agents - Actions].
         ///
-        /// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_14_docs/docs/Learning-Environment-Design-Agents.md#actions
+        /// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_15_docs/docs/Learning-Environment-Design-Agents.md#actions
         /// </remarks>
         /// <param name="actions">
         /// Struct containing the buffers of actions to be executed at this step.

com.unity.ml-agents/Runtime/Demonstrations/DemonstrationRecorder.cs

     /// See [Imitation Learning - Recording Demonstrations] for more information.
     ///
     /// [GameObject]: https://docs.unity3d.com/Manual/GameObjects.html
-    /// [Imitation Learning - Recording Demonstrations]: https://github.com/Unity-Technologies/ml-agents/blob/release_14_docs/docs//Learning-Environment-Design-Agents.md#recording-demonstrations
+    /// [Imitation Learning - Recording Demonstrations]: https://github.com/Unity-Technologies/ml-agents/blob/release_15_docs/docs//Learning-Environment-Design-Agents.md#recording-demonstrations
     /// </remarks>
     [RequireComponent(typeof(Agent))]
     [AddComponentMenu("ML Agents/Demonstration Recorder", (int)MenuGroup.Default)]

com.unity.ml-agents/Runtime/DiscreteActionMasker.cs

         ///
         /// See [Agents - Actions] for more information on masking actions.
         ///
-        /// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_14_docs/docs/Learning-Environment-Design-Agents.md#actions
+        /// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_15_docs/docs/Learning-Environment-Design-Agents.md#actions
         /// </remarks>
         /// <param name="branch">The branch for which the actions will be masked.</param>
         /// <param name="actionIndices">The indices of the masked actions.</param>

com.unity.ml-agents/package.json

 {
   "name": "com.unity.ml-agents",
   "displayName": "ML Agents",
-  "version": "1.8.1-preview",
+  "version": "1.9.0-preview",
   "unity": "2018.4",
   "description": "Use state-of-the-art machine learning to create intelligent character behaviors in any Unity environment (games, robotics, film, etc.).",
   "dependencies": {

docs/Installation-Anaconda-Windows.md

 the ml-agents Conda environment by typing `activate ml-agents`)_:
 
 ```sh
-git clone --branch release_14 https://github.com/Unity-Technologies/ml-agents.git
+git clone --branch release_15 https://github.com/Unity-Technologies/ml-agents.git
-The `--branch release_14` option will switch to the tag of the latest stable
+The `--branch release_15` option will switch to the tag of the latest stable
 release. Omitting that will get the `main` branch which is potentially
 unstable.
 
 connected to the Internet and then type in the Anaconda Prompt:
 
 ```console
-python -m pip install mlagents==0.24.1
+python -m pip install mlagents==0.25.0
 ```
 
 This will complete the installation of all the required Python packages to run
 this, you can try:
 
 ```console
-python -m pip install mlagents==0.24.1 --no-cache-dir
+python -m pip install mlagents==0.25.0 --no-cache-dir
 ```
 
 This `--no-cache-dir` tells the pip to disable the cache.

docs/Installation.md

 the repository if you would like to explore more examples.
 
 ```sh
-git clone --branch release_14 https://github.com/Unity-Technologies/ml-agents.git
+git clone --branch release_15 https://github.com/Unity-Technologies/ml-agents.git
-The `--branch release_14` option will switch to the tag of the latest stable
+The `--branch release_15` option will switch to the tag of the latest stable
 release. Omitting that will get the `main` branch which is potentially unstable.
 
 #### Advanced: Local Installation for Development
-back, make sure to clone the `main` branch (by omitting `--branch release_14`
+back, make sure to clone the `main` branch (by omitting `--branch release_15`
 from the command above). See our
 [Contributions Guidelines](../com.unity.ml-agents/CONTRIBUTING.md) for more
 information on contributing to the ML-Agents Toolkit.
 run from the command line:
 
 ```sh
-python -m pip install mlagents==0.24.1
+python -m pip install mlagents==0.25.0
 ```
 
 Note that this will install `mlagents` from PyPi, _not_ from the cloned

docs/Training-on-Amazon-Web-Service.md

 2. Clone the ML-Agents repo and install the required Python packages
 
    ```sh
-   git clone --branch release_14 https://github.com/Unity-Technologies/ml-agents.git
+   git clone --branch release_15 https://github.com/Unity-Technologies/ml-agents.git
    cd ml-agents/ml-agents/
    pip3 install -e .
    ```

docs/Training-on-Microsoft-Azure.md

    instance, and set it as the working directory.
 2. Install the required packages:
    Torch: `pip3 install torch==1.7.0 -f https://download.pytorch.org/whl/torch_stable.html` and
-   MLAgents: `python -m pip install mlagents==0.24.1`
+   MLAgents: `python -m pip install mlagents==0.25.0`
 
 ## Testing
 

docs/Unity-Inference-Engine.md

 loading expects certain conventions for constants and tensor names. While it is
 possible to construct a model that follows these conventions, we don't provide
 any additional help for this. More details can be found in
-[TensorNames.cs](https://github.com/Unity-Technologies/ml-agents/blob/release_14_docs/com.unity.ml-agents/Runtime/Inference/TensorNames.cs)
+[TensorNames.cs](https://github.com/Unity-Technologies/ml-agents/blob/release_15_docs/com.unity.ml-agents/Runtime/Inference/TensorNames.cs)
-[BarracudaModelParamLoader.cs](https://github.com/Unity-Technologies/ml-agents/blob/release_14_docs/com.unity.ml-agents/Runtime/Inference/BarracudaModelParamLoader.cs).
+[BarracudaModelParamLoader.cs](https://github.com/Unity-Technologies/ml-agents/blob/release_15_docs/com.unity.ml-agents/Runtime/Inference/BarracudaModelParamLoader.cs).
 
 If you wish to run inference on an externally trained model, you should use
 Barracuda directly, instead of trying to run it through ML-Agents.

gym-unity/gym_unity/__init__.py

 # Version of the library that will be used to upload to pypi
-__version__ = "0.25.0.dev0"
+__version__ = "0.25.0"
-__release_tag__ = None
+__release_tag__ = "release_15"

ml-agents-envs/README.md

 Install the `mlagents_envs` package with:
 
 ```sh
-python -m pip install mlagents_envs==0.24.1
+python -m pip install mlagents_envs==0.25.0
 ```
 
 ## Usage & More Information

ml-agents-envs/mlagents_envs/__init__.py

 # Version of the library that will be used to upload to pypi
-__version__ = "0.25.0.dev0"
+__version__ = "0.25.0"
-__release_tag__ = None
+__release_tag__ = "release_15"

ml-agents/README.md

 Install the `mlagents` package with:
 
 ```sh
-python -m pip install mlagents==0.24.1
+python -m pip install mlagents==0.25.0
 ```
 
 ## Usage & More Information

ml-agents/mlagents/trainers/__init__.py

 # Version of the library that will be used to upload to pypi
-__version__ = "0.25.0.dev0"
+__version__ = "0.25.0"
-__release_tag__ = None
+__release_tag__ = "release_15"

utils/validate_release_links.py

 # It matches "mlagents" and "mlagents_envs", accessible as group "package"
 # and optionally matches the version, e.g. "==1.2.3"
 PIP_INSTALL_PATTERN = re.compile(
-    r"(python -m )?pip3* install (?P<package>mlagents(_envs)?)(==[0-9]\.[0-9]\.[0-9](\.dev[0-9]+)?)?"
+    r"(python -m )?pip3* install (?P<package>mlagents(_envs)?)(==[0-9]+\.[0-9]+\.[0-9]+(\.dev[0-9]+)?)?"
 )
 TRAINER_INIT_FILE = "ml-agents/mlagents/trainers/__init__.py"
 
         ("python -m pip install mlagents", True),
         ("python -m pip install mlagents==1.2.3", True),
         ("python -m pip install mlagents_envs==1.2.3", True),
+        ("python -m pip install mlagents==11.222.3333", True),
+        ("python -m pip install mlagents_envs==11.222.3333", True),
     ]:
         assert bool(PIP_INSTALL_PATTERN.search(s)) is expected
 
 
 def update_pip_install_line(line, package_verion):
     match = PIP_INSTALL_PATTERN.search(line)
-    package_name = match.group("package")
-    replacement_version = f"python -m pip install {package_name}=={package_verion}"
-    updated = PIP_INSTALL_PATTERN.sub(replacement_version, line)
-    return updated
+    if match is not None:  # if there is a pip install line
+        package_name = match.group("package")
+        replacement_version = f"python -m pip install {package_name}=={package_verion}"
+        updated = PIP_INSTALL_PATTERN.sub(replacement_version, line)
+        return updated
+    else:  # Don't do anything
+        return line
 
 
 def git_ls_files() -> List[str]: