Convert relative path to github url

maryamziaa · maryamziaa · commit fb1bb2723004 · 2025-08-04T18:18:34.000-04:00
diff --git a/com.unity.ml-agents/Documentation~/CODE_OF_CONDUCT.md b/com.unity.ml-agents/Documentation~/CODE_OF_CONDUCT.md
diff --git a/com.unity.ml-agents/Documentation~/CONTRIBUTING.md b/com.unity.ml-agents/Documentation~/CONTRIBUTING.md
@@ -1 +1,36 @@
-{!../../CONTRIBUTING.md!}
+# How to Contribute to ML-Agents
+
+## 1.Fork the repository
+Fork the ML-Agents repository by clicking on the "Fork" button in the top right corner of the GitHub page. This creates a copy of the repository under your GitHub account.
+
+## 2. Set up your development environment
+Clone the forked repository to your local machine using Git. Install the necessary dependencies and follow the instructions provided in the project's documentation to set up your development environment properly.
+
+## 3. Choose an issue or feature
+Browse the project's issue tracker or discussions to find an open issue or feature that you would like to contribute to. Read the guidelines and comments associated with the issue to understand the requirements and constraints.
+
+## 4. Make your changes
+Create a new branch for your changes based on the main branch of the ML-Agents repository. Implement your code changes or add new features as necessary. Ensure that your code follows the project's coding style and conventions.
+
+* Example: Let's say you want to add support for a new type of reward function in the ML-Agents framework. You can create a new branch named feature/reward-function to implement this feature.
+
+## 5. Test your changes
+Run the appropriate tests to ensure your changes work as intended. If necessary, add new tests to cover your code and verify that it doesn't introduce regressions.
+
+* Example: For the reward function feature, you would write tests to check different scenarios and expected outcomes of the new reward function.
+
+## 6. Submit a pull request
+Push your branch to your forked repository and submit a pull request (PR) to the ML-Agents main repository. Provide a clear and concise description of your changes, explaining the problem you solved or the feature you added.
+
+* Example: In the pull request description, you would explain how the new reward function works, its benefits, and any relevant implementation details.
+
+## 7. Respond to feedback
+Be responsive to any feedback or comments provided by the project maintainers. Address the feedback by making necessary revisions to your code and continue the discussion if required.
+
+## 8. Continuous integration and code review
+The ML-Agents project utilizes automated continuous integration (CI) systems to run tests on pull requests. Address any issues flagged by the CI system and actively participate in the code review process by addressing comments from reviewers.
+
+## 9. Merge your changes
+Once your pull request has been approved and meets all the project's requirements, a project maintainer will merge your changes into the main repository. Congratulations, your contribution has been successfully integrated!
+
+**Remember to always adhere to the project's code of conduct, be respectful, and follow any specific contribution guidelines provided by the ML-Agents project. Happy contributing!**
diff --git a/com.unity.ml-agents/Documentation~/Installation.md b/com.unity.ml-agents/Documentation~/Installation.md
@@ -151,8 +151,8 @@ conda install "grpcio=1.48.2" -c conda-forge
 Note, you need to have the matching version of
 the Unity packages with the particular release of the python packages. You can find the release history [here](https://github.com/Unity-Technologies/ml-agents/releases)
 
-By installing the `mlagents` package, the dependencies listed in the
-[setup.py file](../../ml-agents/setup.py) are also installed. These include
+When you install the `mlagents` package, the dependencies listed in the
+[setup.py file](https://github.com/Unity-Technologies/ml-agents/blob/release_22/ml-agents/setup.py) are also installed. These include
 [PyTorch](Background-PyTorch.md).
 
 If you intend to make modifications to `mlagents` or `mlagents_envs`, you should
diff --git a/com.unity.ml-agents/Documentation~/LICENSE.md b/com.unity.ml-agents/Documentation~/LICENSE.md
diff --git a/com.unity.ml-agents/Documentation~/Learning-Environment-Design.md b/com.unity.ml-agents/Documentation~/Learning-Environment-Design.md
@@ -118,7 +118,7 @@ for curriculum learning and environment parameter randomization for details.
 We recommend modifying the environment from the Agent's `OnEpisodeBegin()`
 function by leveraging `Academy.Instance.EnvironmentParameters`. See the
 WallJump example environment for a sample usage (specifically,
-[WallJumpAgent.cs](../../Project/Assets/ML-Agents/Examples/WallJump/Scripts/WallJumpAgent.cs)
+[WallJumpAgent.cs](https://github.com/Unity-Technologies/ml-agents/blob/release_22/Project/Assets/ML-Agents/Examples/WallJump/Scripts/WallJumpAgent.cs)
 ).
 
 ## Agent
@@ -158,5 +158,5 @@ environments. These statistics are aggregated and generated during the training
 process. To record statistics, see the `StatsRecorder` C# class.
 
 See the FoodCollector example environment for a sample usage (specifically,
-[FoodCollectorSettings.cs](../../Project/Assets/ML-Agents/Examples/FoodCollector/Scripts/FoodCollectorSettings.cs)
+[FoodCollectorSettings.cs](https://github.com/Unity-Technologies/ml-agents/blob/release_22/Project/Assets/ML-Agents/Examples/FoodCollector/Scripts/FoodCollectorSettings.cs)
 ).
diff --git a/com.unity.ml-agents/Documentation~/Limitations.md b/com.unity.ml-agents/Documentation~/Limitations.md
@@ -3,5 +3,5 @@
 See the package-specific Limitations pages:
 
 - [`com.unity.mlagents` Unity package](Package-Limitations.md)
-- [`mlagents` Python package](../../ml-agents/README.md#limitations)
-- [`mlagents_envs` Python package](../../ml-agents-envs/README.md#limitations)
+- [`mlagents` Python package](https://github.com/Unity-Technologies/ml-agents/blob/release_22/ml-agents/README.md#limitations)
+- [`mlagents_envs` Python package](https://github.com/Unity-Technologies/ml-agents/blob/release_22/ml-agents-envs/README.md#limitations)
diff --git a/com.unity.ml-agents/Documentation~/Python-Custom-Trainer-Plugin.md b/com.unity.ml-agents/Documentation~/Python-Custom-Trainer-Plugin.md
@@ -10,7 +10,7 @@ information on how python plugin system works see [Plugin interfaces](Training-P
 Model-free RL algorithms generally fall into two broad categories: on-policy and off-policy. On-policy algorithms perform updates based on data gathered from the current policy. Off-policy algorithms learn a Q function from a buffer of previous data, then use this Q function to make decisions. Off-policy algorithms have three key benefits in the context of ML-Agents: They tend to use fewer samples than on-policy as they can pull and re-use data from the buffer many times. They allow player demonstrations to be inserted in-line with RL data into the buffer, enabling new ways of doing imitation learning by streaming player data.
 
 To add new custom trainers to ML-agents, you would need to create a new python package.
-To give you an idea of how to structure your package, we have created a [mlagents_trainer_plugin](../ml-agents-trainer-plugin) package ourselves as an
+To give you an idea of how to structure your package, we have created a [mlagents_trainer_plugin](https://github.com/Unity-Technologies/ml-agents/tree/release_22/ml-agents-trainer-plugin) package ourselves as an
 example, with implementation of `A2c` and `DQN` algorithms. You would need a `setup.py` file to list extra requirements and
 register the new RL algorithm in ml-agents ecosystem and be able to call `mlagents-learn` CLI with your customized
 configuration.
diff --git a/com.unity.ml-agents/Documentation~/Python-Gym-API.md b/com.unity.ml-agents/Documentation~/Python-Gym-API.md
@@ -12,7 +12,7 @@ Unity environment via Python.
 ## Installation
 
 The gym wrapper is part of the `mlagents_envs` package. Please refer to the
-[mlagents_envs installation instructions](../../ml-agents-envs/README.md).
+[mlagents_envs installation instructions](https://github.com/Unity-Technologies/ml-agents/blob/release_22/ml-agents-envs/README.md).
 
 
 ## Using the Gym Wrapper
diff --git a/com.unity.ml-agents/Documentation~/Python-LLAPI.md b/com.unity.ml-agents/Documentation~/Python-LLAPI.md
@@ -39,7 +39,7 @@ The key objects in the Python API include:
   DecisionSteps and TerminalSteps as well as the expected action shapes.
 
 These classes are all defined in the
-[base_env](../../ml-agents-envs/mlagents_envs/base_env.py) script.
+[base_env](https://github.com/Unity-Technologies/ml-agents/blob/release_22/ml-agents-envs/mlagents_envs/base_env.py) script.
 
 An Agent "Behavior" is a group of Agents identified by a `BehaviorName` that
 share the same observations and action types (described in their
@@ -59,7 +59,7 @@ release._
 ## Loading a Unity Environment
 
 Python-side communication happens through `UnityEnvironment` which is located in
-[`environment.py`](../ml-agents-envs/mlagents_envs/environment.py). To load a
+[`environment.py`](https://github.com/Unity-Technologies/ml-agents/blob/release_22/ml-agents-envs/mlagents_envs/environment.py). To load a
 Unity environment from a built binary file, put the file in the same directory
 as `envs`. For example, if the filename of your Unity environment is `3DBall`,
 in python, run:
diff --git a/com.unity.ml-agents/Documentation~/Python-PettingZoo-API.md b/com.unity.ml-agents/Documentation~/Python-PettingZoo-API.md
@@ -8,7 +8,7 @@ interfacing with a Unity environment via Python.
 ## Installation and Examples
 
 The PettingZoo wrapper is part of the `mlagents_envs` package. Please refer to the
-[mlagents_envs installation instructions](../../ml-agents-envs/README.md).
+[mlagents_envs installation instructions](https://github.com/Unity-Technologies/ml-agents/blob/release_22/ml-agents-envs/README.md).
 
 [[Colab] PettingZoo Wrapper Example](https://colab.research.google.com/github/Unity-Technologies/ml-agents/blob/develop-python-api-ga/ml-agents-envs/colabs/Colab_PettingZoo.ipynb)
 
diff --git a/com.unity.ml-agents/Documentation~/Readme.md b/com.unity.ml-agents/Documentation~/Readme.md
@@ -2,7 +2,7 @@
 
 [![docs badge](https://img.shields.io/badge/docs-reference-blue.svg)](https://github.com/Unity-Technologies/ml-agents/tree/release_22_docs/docs/)
 
-[![license badge](https://img.shields.io/badge/license-Apache--2.0-green.svg)](LICENSE.md)
+[![license badge](https://img.shields.io/badge/license-Apache--2.0-green.svg)](https://github.com/Unity-Technologies/ml-agents/blob/release_22/LICENSE.md)
 
 ([latest release](https://github.com/Unity-Technologies/ml-agents/releases/tag/latest_release))
 ([all releases](https://github.com/Unity-Technologies/ml-agents/releases))
@@ -174,7 +174,7 @@ We have also published a series of blog posts that are relevant for ML-Agents:
 The ML-Agents Toolkit is an open-source project and we encourage and welcome
 contributions. If you wish to contribute, be sure to review our
 [contribution guidelines](CONTRIBUTING.md) and
-[code of conduct](CODE_OF_CONDUCT.md).
+[code of conduct](https://github.com/Unity-Technologies/ml-agents/blob/release_22/CODE_OF_CONDUCT.md).
 
 For problems with the installation and setup of the ML-Agents Toolkit, or
 discussions about how to best setup or train your agents, please create a new
diff --git a/com.unity.ml-agents/Documentation~/Training-ML-Agents.md b/com.unity.ml-agents/Documentation~/Training-ML-Agents.md
@@ -6,7 +6,7 @@ powered by a dedicated Python package, `mlagents`. This package exposes a
 command `mlagents-learn` that is the single entry point for all training
 workflows (e.g. reinforcement leaning, imitation learning, curriculum learning).
 Its implementation can be found at
-[ml-agents/mlagents/trainers/learn.py](../../ml-agents/mlagents/trainers/learn.py).
+[ml-agents/mlagents/trainers/learn.py](https://github.com/Unity-Technologies/ml-agents/blob/release_22/ml-agents/mlagents/trainers/learn.py).
 
 ## Training with mlagents-learn
 
@@ -130,7 +130,7 @@ ML-Agents Toolkit involves tuning the training hyperparameters and
 configuration. This guide contains some best practices for tuning the training
 process when the default parameters don't seem to be giving the level of
 performance you would like. We provide sample configuration files for our
-example environments in the [config/](../../config/) directory. The
+example environments in the [config/](https://github.com/Unity-Technologies/ml-agents/tree/release_22/config/) directory. The
 `config/ppo/3DBall.yaml` was used to train the 3D Balance Ball in the
 [Running an Example](Sample.md) guide. That configuration file uses the
 PPO trainer, but we also have configuration files for SAC and GAIL.
diff --git a/com.unity.ml-agents/Documentation~/Training-Plugins.md b/com.unity.ml-agents/Documentation~/Training-Plugins.md
@@ -14,7 +14,7 @@ starting point.
 
 ### setup.py
 If you don't already have a `setup.py` file for your python code, you'll need to add one. `ml-agents-plugin-examples`
-has a [minimal example](../../ml-agents-plugin-examples/setup.py) of this.
+has a [minimal example](https://github.com/Unity-Technologies/ml-agents/blob/release_22/ml-agents-plugin-examples/setup.py) of this.
 
 In the call to `setup()`, you'll need to add to the `entry_points` dictionary for each plugin interface that you
 implement. The form of this is `{entry point name}={plugin module}:{plugin function}`. For example, in
@@ -56,4 +56,4 @@ emission.
 
 #### Registration
 The `StatsWriter` registration function takes a `RunOptions` argument and returns a list of `StatsWriter`s. An
-example implementation is provided in [`mlagents_plugin_examples`](../ml-agents-plugin-examples/mlagents_plugin_examples/example_stats_writer.py)
+example implementation is provided in [`mlagents_plugin_examples`](https://github.com/Unity-Technologies/ml-agents/blob/release_22/ml-agents-plugin-examples/mlagents_plugin_examples/example_stats_writer.py)
diff --git a/com.unity.ml-agents/Documentation~/Tutorial-Custom-Trainer-Plugin.md b/com.unity.ml-agents/Documentation~/Tutorial-Custom-Trainer-Plugin.md
@@ -11,7 +11,7 @@ conda activate trainer-env
 
 Users of the plug-in system are responsible for implementing the trainer class subject to the API standard. Let us follow an example by implementing a custom trainer named "YourCustomTrainer". You can either extend `OnPolicyTrainer` or `OffPolicyTrainer` classes depending on the training strategies you choose.
 
-Please refer to the internal [PPO implementation](../../ml-agents/mlagents/trainers/ppo/trainer.py) for a complete code example. We will not provide a workable code in the document. The purpose of the tutorial is to introduce you to the core components and interfaces of our plugin framework. We use code snippets and patterns to demonstrate the control and data flow.
+Please refer to the internal [PPO implementation](https://github.com/Unity-Technologies/ml-agents/blob/release_22/ml-agents/mlagents/trainers/ppo/trainer.py) for a complete code example. We will not provide a workable code in the document. The purpose of the tutorial is to introduce you to the core components and interfaces of our plugin framework. We use code snippets and patterns to demonstrate the control and data flow.
 
 Your custom trainers are responsible for collecting experiences and training the models. Your custom trainer class acts like a coordinator to the policy and optimizer. To start implementing methods in the class, create a policy class objects from method `create_policy`:
 
diff --git a/com.unity.ml-agents/Documentation~/index.md b/com.unity.ml-agents/Documentation~/index.md
@@ -35,7 +35,7 @@ The package allows you to convert any Unity scene into a learning environment an
 The ML-Agents Toolkit is an open-source project, and we encourage and welcome
 contributions. If you wish to contribute, be sure to review our
 [contribution guidelines](CONTRIBUTING.md) and
-[code of conduct](CODE_OF_CONDUCT.md).
+[code of conduct](https://github.com/Unity-Technologies/ml-agents/blob/release_22/CODE_OF_CONDUCT.md).
 
 For problems with the installation and setup of the ML-Agents Toolkit, or
 discussions about how to best setup or train your agents, please create a new
