Unity-Technologies
diff --git a/‎.pre-commit-config.yaml
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diff --git a/‎conftest.py
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diff --git a/‎docs/Python-Custom-Trainer-Plugin.md
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@@ -128,6 +128,6 @@ repos:
     -   id: generate-markdown-docs
         name: generate markdown docs
         language: python
-        entry: ./utils/generate_markdown_docs.py --package_dirs ml-agents-envs
+        entry: ./utils/generate_markdown_docs.py --package_dirs ml-agents-envs ml-agents
         pass_filenames: false
         additional_dependencies: [pyyaml, pydoc-markdown==3.10.1]
@@ -14,6 +14,7 @@
 from filelock import FileLock
 
 # TODO: Use this in all ml-agents tests so they can all run in parallel.
+import mlagents.plugins.trainer_type
 
 _BASE_PORT = 6005
 
@@ -76,3 +77,8 @@ def test_something(base_port: int) -> None:
     :return: The base port number.
     """
     return PortAllocator().reserve_n_ports(n_ports)
+
+
+@pytest.fixture(scope="session", autouse=True)
+def setup_plugin_trainers():
+    _, _ = mlagents.plugins.trainer_type.register_trainer_plugins()
@@ -0,0 +1,52 @@
+# Unity Ml-Agents Custom trainers Plugin
+
+As an attempt to bring a wider variety of reinforcement learning algorithms to our users, we have added custom trainers
+capabilities. we introduce an extensible plugin system to define new trainers based on the High level trainer API
+in `Ml-agents` Package. This will allow rerouting `mlagents-learn` CLI to custom trainers and extending the config files
+with hyper-parameters specific to your new trainers. We will expose a high-level extensible trainer (both on-policy,
+and off-policy trainers) optimizer and hyperparameter classes with documentation for the use of this plugin. For more
+infromation on how python plugin system works see [Plugin interfaces](Training-Plugins.md).
+
+## Overview
+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
+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.
+
+
+```shell
+├── mlagents_trainer_plugin
+│    ├── __init__.py
+│    ├── a2c
+│    │    ├── __init__.py
+│    │    ├── a2c_3DBall.yaml
+│    │    ├── a2c_optimizer.py
+│    │    └── a2c_trainer.py
+│    └── dqn
+│        ├── __init__.py
+│        ├── dqn_basic.yaml
+│        ├── dqn_optimizer.py
+│        └── dqn_trainer.py
+└── setup.py
+```
+## Installation and Execution
+To install your new package, you need to have `ml-agents-env` and `ml-agents` installed following by the installation of
+plugin package.
+
+```shell
+> pip3 install -e ./ml-agents-envs && pip3 install -e ./ml-agents
+> pip install -e <./ml-agents-trainer-plugin>
+```
+
+Following the previous installations your package is added as an entrypoint and you can use a config file with new
+trainers:
+```shell
+mlagents-learn ml-agents-trainer-plugin/mlagents_trainer_plugin/a2c/a2c_3DBall.yaml --run-id <run-id-name>
+--env <env-executable>
+```
+
+## Tutorial
+Here’s a step-by-step [tutorial](.) on how to write a setup file and extend ml-agents trainers, optimizers, and
+hyperparameter settings.To extend ML-agents classes see references on
+[trainers](Python-On-Off-Policy-Trainer-Documentation.md) and [Optimizer](Python-Optimizer-Documentation.md).