Use develop branch for links

Author: maryamziaa

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

@@ -26,9 +26,9 @@
   * [On/Off Policy Trainer Documentation](Python-On-Off-Policy-Trainer-Documentation.md)
   * [Python Optimizer Documentation](Python-Optimizer-Documentation.md)
 * [Python Tutorial with Google Colab](Tutorial-Colab.md)
-  * [Using a UnityEnvironment](https://colab.research.google.com/github/Unity-Technologies/ml-agents/blob/release_22_docs/colab/Colab_UnityEnvironment_1_Run.ipynb)
-  * [Q-Learning with a UnityEnvironment](https://colab.research.google.com/github/Unity-Technologies/ml-agents/blob/release_22_docs/colab/Colab_UnityEnvironment_2_Train.ipynb)
-  * [Using Side Channels on a UnityEnvironment](https://colab.research.google.com/github/Unity-Technologies/ml-agents/blob/release_22_docs/colab/Colab_UnityEnvironment_3_SideChannel.ipynb)
+  * [Using a UnityEnvironment](https://colab.research.google.com/github/Unity-Technologies/ml-agents/blob/develop/colab/Colab_UnityEnvironment_1_Run.ipynb)
+  * [Q-Learning with a UnityEnvironment](https://colab.research.google.com/github/Unity-Technologies/ml-agents/blob/develop/colab/Colab_UnityEnvironment_2_Train.ipynb)
+  * [Using Side Channels on a UnityEnvironment](https://colab.research.google.com/github/Unity-Technologies/ml-agents/blob/develop/colab/Colab_UnityEnvironment_3_SideChannel.ipynb)
 * [Advanced Features](Advanced-Features.md)
   * [Custom Side Channels](Custom-SideChannels.md)
   * [Custom Grid Sensors](Custom-GridSensors.md)

com.unity.ml-agents/Documentation~/Training-ML-Agents.md

@@ -62,7 +62,7 @@ More specifically, this section offers a detailed guide on the command-line flag
 
 - `<trainer-config-file>`: defines the training hyperparameters for each Behavior in the scene, and the set-ups for the environment parameters (Curriculum Learning and Environment Parameter Randomization)
 
-It is important to highlight that successfully training a Behavior in the 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](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.
+It is important to highlight that successfully training a Behavior in the 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](https://github.com/Unity-Technologies/ml-agents/tree/develop/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.
 
 Additionally, the set of configurations you provide depend on the training functionalities you use (see [ML-Agents Theory](ML-Agents-Overview.md) for a description of all the training functionalities). Each functionality you add typically has its own training configurations. For instance:
 

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

@@ -4,6 +4,6 @@ Interactive tutorials for using ML-Agents with Google Colab environments.
 
 | **Tutorial**                                                                                                                                                                              | **Description**                                                    |
 |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------|
-| [Using a UnityEnvironment](https://colab.research.google.com/github/Unity-Technologies/ml-agents/blob/release_22_docs/colab/Colab_UnityEnvironment_1_Run.ipynb)                           | Learn how to set up and interact with Unity environments in Colab. |
-| [Q-Learning with a UnityEnvironment](https://colab.research.google.com/github/Unity-Technologies/ml-agents/blob/release_22_docs/colab/Colab_UnityEnvironment_2_Train.ipynb)               | Implement Q-Learning algorithms with Unity ML-Agents in Colab.     |
-| [Using Side Channels on a UnityEnvironment](https://colab.research.google.com/github/Unity-Technologies/ml-agents/blob/release_22_docs/colab/Colab_UnityEnvironment_3_SideChannel.ipynb)  | Explore side channel communication between Unity and Python.       |
+| [Using a UnityEnvironment](https://colab.research.google.com/github/Unity-Technologies/ml-agents/blob/develop/colab/Colab_UnityEnvironment_1_Run.ipynb)                           | Learn how to set up and interact with Unity environments in Colab. |
+| [Q-Learning with a UnityEnvironment](https://colab.research.google.com/github/Unity-Technologies/ml-agents/blob/develop/colab/Colab_UnityEnvironment_2_Train.ipynb)               | Implement Q-Learning algorithms with Unity ML-Agents in Colab.     |
+| [Using Side Channels on a UnityEnvironment](https://colab.research.google.com/github/Unity-Technologies/ml-agents/blob/develop/colab/Colab_UnityEnvironment_3_SideChannel.ipynb)  | Explore side channel communication between Unity and Python.       |

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

@@ -31,7 +31,7 @@ The package allows you to convert any Unity scene into a learning environment an
 
 ## Community and Feedback
 
-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](https://github.com/Unity-Technologies/ml-agents/blob/release_22/CODE_OF_CONDUCT.md).
+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](https://github.com/Unity-Technologies/ml-agents/blob/develop/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 thread on the [Unity ML-Agents discussion forum](https://discussions.unity.com/tag/ml-agents). Be sure to include as many details as possible to help others assist you effectively. If you run into any other problems using the ML-Agents Toolkit or have a specific feature request, please [submit a GitHub issue](https://github.com/Unity-Technologies/ml-agents/issues).
 

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

@@ -184,7 +184,7 @@ public interface IActionReceiver
         ///
         /// See [Agents - Actions] for more information on masking actions.
         ///
-        /// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/develop/com.unity.ml-agents/Documentation~//Learning-Environment-Design-Agents.md
+        /// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/develop/com.unity.ml-agents/Documentation~/Learning-Environment-Design-Agents.md
         /// </remarks>
         /// <seealso cref="IActionReceiver.OnActionReceived"/>
         void WriteDiscreteActionMask(IDiscreteActionMask actionMask);