Doc link fix (#3865)

* Make all doc links point to release_1_docs tag

* fix 0.15.1 link

* relative links in readme

* fix link in env warnings

* more link fixes

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/latest_release/docs/Installation.md
+[installation instructions]: https://github.com/Unity-Technologies/ml-agents/blob/release_1_docs/docs/Installation.md
 [GitHUb Repository]: https://github.com/Unity-Technologies/ml-agents
 [Python package]: https://github.com/Unity-Technologies/ml-agents
 [Execution Order of Event Functions]: https://docs.unity3d.com/Manual/ExecutionOrder.html

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/blob/0.15.1/docs/
+ * https://github.com/Unity-Technologies/ml-agents/tree/release_1_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/blob/master/" +
+    [HelpURL("https://github.com/Unity-Technologies/ml-agents/tree/release_1_docs/" +
         "docs/Learning-Environment-Design.md")]
     public class Academy : IDisposable
     {

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/0.15.1/docs/Learning-Environment-Design-Agents.md
-    /// [Reinforcement Learning in Unity]: https://github.com/Unity-Technologies/ml-agents/blob/0.15.1/docs/Learning-Environment-Design.md
+    /// [Agents]: https://github.com/Unity-Technologies/ml-agents/blob/release_1_docs/docs/Learning-Environment-Design-Agents.md
+    /// [Reinforcement Learning in Unity]: https://github.com/Unity-Technologies/ml-agents/blob/release_1_docs/docs/Learning-Environment-Design.md
-    /// [Unity ML-Agents Toolkit manual]: https://github.com/Unity-Technologies/ml-agents/blob/0.15.1/docs/Readme.md
+    /// [Unity ML-Agents Toolkit manual]: https://github.com/Unity-Technologies/ml-agents/blob/release_1_docs/docs/Readme.md
-    [HelpURL("https://github.com/Unity-Technologies/ml-agents/blob/master/" +
+    [HelpURL("https://github.com/Unity-Technologies/ml-agents/blob/release_1_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/0.15.1/docs/Learning-Environment-Design-Agents.md#rewards
-        /// [Reward Signals]: https://github.com/Unity-Technologies/ml-agents/blob/0.15.1/docs/Reward-Signals.md
+        /// [Agents - Rewards]: https://github.com/Unity-Technologies/ml-agents/blob/release_1_docs/docs/Learning-Environment-Design-Agents.md#rewards
+        /// [Reward Signals]: https://github.com/Unity-Technologies/ml-agents/blob/release_1_docs/docs/Reward-Signals.md
         /// </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/0.15.1/docs/Learning-Environment-Design-Agents.md#rewards
-        /// [Reward Signals]: https://github.com/Unity-Technologies/ml-agents/blob/0.15.1/docs/Reward-Signals.md
+        /// [Agents - Rewards]: https://github.com/Unity-Technologies/ml-agents/blob/release_1_docs/docs/Learning-Environment-Design-Agents.md#rewards
+        /// [Reward Signals]: https://github.com/Unity-Technologies/ml-agents/blob/release_1_docs/docs/Reward-Signals.md
         ///</remarks>
         /// <param name="increment">Incremental reward value.</param>
         public void AddReward(float increment)
         /// control of an agent using keyboard, mouse, or game controller input.
         ///
         /// Your heuristic implementation can use any decision making logic you specify. Assign decision
-        /// values to the float[] array, <paramref cref="actionsOut"/>, passed to your function as a parameter.
+        /// values to the float[] array, <paramref name="actionsOut"/>, passed to your function as a parameter.
         /// Add values to the array at the same indexes as they are used in your
         /// <seealso cref="OnActionReceived(float[])"/> function, which receives this array and
         /// implements the corresponding agent behavior. See [Actions] for more information
         /// 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/0.15.1/docs/Training-Imitation-Learning.md#recording-demonstrations
-        /// [Actions]: https://github.com/Unity-Technologies/ml-agents/blob/0.15.1/docs/Learning-Environment-Design-Agents.md#actions
+        /// [Demonstration Recorder]: https://github.com/Unity-Technologies/ml-agents/blob/release_1_docs/docs/Training-Imitation-Learning.md#recording-demonstrations
+        /// [Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_1_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/0.15.1/docs/Learning-Environment-Design-Agents.md#observations-and-sensors
+        /// [Observations and Sensors]: https://github.com/Unity-Technologies/ml-agents/blob/release_1_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/master/docs/Learning-Environment-Design-Agents.md#actions
+        /// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_1_docs/docs/Learning-Environment-Design-Agents.md#actions
         /// </remarks>
         /// <seealso cref="OnActionReceived(float[])"/>
         public virtual void CollectDiscreteActionMasks(DiscreteActionMasker actionMasker)
         ///
         /// For more information about implementing agent actions see [Agents - Actions].
         ///
-        /// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/0.15.1/docs/Learning-Environment-Design-Agents.md#actions
+        /// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_1_docs/docs/Learning-Environment-Design-Agents.md#actions
         /// </remarks>
         /// <param name="vectorAction">
         /// An array containing the action vector. The length of the array is specified

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/0.15.1/docs/Training-Imitation-Learning.md#recording-demonstrations
+    /// [Imitation Learning - Recording Demonstrations]: https://github.com/Unity-Technologies/ml-agents/blob/release_1_docs/docs/Training-Imitation-Learning.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/0.15.1/docs/Learning-Environment-Design-Agents.md#actions
+        /// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_1_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>

docs/Installation-Anaconda-Windows.md

 release. Omitting that will get the `master` branch which is potentially
 unstable.
 
-If you don't want to use Git, you can always directly download all the files
-[here](https://github.com/Unity-Technologies/ml-agents/archive/latest_release.zip).
+If you don't want to use Git, you can find download links on the
+[releases page](https://github.com/Unity-Technologies/ml-agents/releases).
 
 The `com.unity.ml-agents` subdirectory contains the core code to add to your
 projects. The `Project` subdirectory contains many

docs/Training-Curriculum-Learning.md

 
 Once our curriculum is defined, we have to use the environment parameters we defined
 and modify the environment from the Agent's `OnEpisodeBegin()` function. See
-[WallJumpAgent.cs](https://github.com/Unity-Technologies/ml-agents/blob/master/Project/Assets/ML-Agents/Examples/WallJump/Scripts/WallJumpAgent.cs)
+[WallJumpAgent.cs](../Project/Assets/ML-Agents/Examples/WallJump/Scripts/WallJumpAgent.cs)
 for an example.
 
 

ml-agents-envs/README.md

 
 * `mlagents_envs`: A low level API which allows you to interact directly with a
   Unity Environment. See
-  [here](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Python-API.md)
+  [here](../docs/Python-API.md)
   for more information on using this package.
 
 ## Installation
 ## Usage & More Information
 
 For more detailed documentation, check out the
-[ML-Agents Toolkit documentation.](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Readme.md)
+[ML-Agents Toolkit documentation.](../docs/Readme.md)

ml-agents-envs/mlagents_envs/environment.py

         raise UnityEnvironmentException(
             f"The communication API version is not compatible between Unity and python. "
             f"Python API: {UnityEnvironment.API_VERSION}, Unity API: {unity_com_ver}.\n "
-            f"Please go to https://github.com/Unity-Technologies/ml-agents/releases/tag/latest_release "
-            f"to download the latest version of ML-Agents."
+            f"Please find the versions that work best together from our release page.\n"
+            "https://github.com/Unity-Technologies/ml-agents/releases"
         )
 
     @staticmethod

ml-agents/README.md

 
 * `mlagents_envs`: A low level API which allows you to interact directly with a
   Unity Environment. See
-  [here](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Python-API.md)
+  [here](../docs/Python-API.md)
-  [here](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Training-ML-Agents.md)
+  [here](../docs/Training-ML-Agents.md)
   for more information on using this package.
 
 ## Installation
 ## Usage & More Information
 
 For more detailed documentation, check out the
-[ML-Agents Toolkit documentation.](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Readme.md)
+[ML-Agents Toolkit documentation.](../docs/Readme.md)