diff --git a/docs/08_authors.rst b/docs/08_authors.rst
index c7558862a35ab54880c763cf1c61f7c271e639d5..0661b54dc766ced990aee5a2232e6c102dcae3b7 100644
--- a/docs/08_authors.rst
+++ b/docs/08_authors.rst
@@ -1,6 +1,4 @@
Authors
=======
-.. toctree::
- :maxdepth: 2
.. include:: ../AUTHORS.rst
diff --git a/docs/10_interface.rst b/docs/10_interface.rst
new file mode 100644
index 0000000000000000000000000000000000000000..51464bc9345e59512abdec69a7f260bb8f9c7e5d
--- /dev/null
+++ b/docs/10_interface.rst
@@ -0,0 +1,33 @@
+Multi-Agent Interface
+=======
+
+.. include:: interface/pettingzoo.rst
+.. include:: interface/wrappers.rst
+
+Multi-Agent Pettingzoo Usage
+=======
+
+We can use the PettingZoo interface by proving the rail env to the petting zoo wrapper as shown below in the example.
+
+.. literalinclude:: ../tests/test_pettingzoo_interface.py
+ :language: python
+ :start-after: __sphinx_doc_begin__
+ :end-before: __sphinx_doc_end__
+
+
+Multi-Agent Interface Stable Baseline 3 Training
+=======
+
+.. literalinclude:: ../flatland/contrib/training/flatland_pettingzoo_stable_baselines.py
+ :language: python
+ :start-after: __sphinx_doc_begin__
+ :end-before: __sphinx_doc_end__
+
+
+Multi-Agent Interface Rllib Training
+=======
+
+.. literalinclude:: ../flatland/contrib/training/flatland_pettingzoo_rllib.py
+ :language: python
+ :start-after: __sphinx_doc_begin__
+ :end-before: __sphinx_doc_end__
\ No newline at end of file
diff --git a/docs/10_interface_toc.rst b/docs/10_interface_toc.rst
new file mode 100644
index 0000000000000000000000000000000000000000..88d2ad7a5b139b00f123045d11c0d1d91e5e6624
--- /dev/null
+++ b/docs/10_interface_toc.rst
@@ -0,0 +1,8 @@
+Multi-Agent Interfaces
+==============
+
+
+.. toctree::
+ :maxdepth: 2
+
+ 10_interface
diff --git a/docs/index.rst b/docs/index.rst
index 852ef7f31d32f79f4a0537f5db8a942d8c35c841..e60b80971d4ac74672e6f36037a384a4e31e52d9 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -15,6 +15,7 @@ Welcome to flatland's documentation!
07_changes
08_authors
09_faq_toc
+ 10_interface
Indices and tables
==================
diff --git a/docs/interface/pettingzoo.md b/docs/interface/pettingzoo.md
new file mode 100644
index 0000000000000000000000000000000000000000..57d5ea7683b5ab99cffdbb8f1d0dc1a508f4d715
--- /dev/null
+++ b/docs/interface/pettingzoo.md
@@ -0,0 +1,26 @@
+# PettingZoo
+
+> PettingZoo (https://www.pettingzoo.ml/) is a collection of multi-agent environments for reinforcement learning. We build a pettingzoo interface for flatland.
+
+## Background
+
+PettingZoo is a popular multi-agent environment library (https://arxiv.org/abs/2009.14471) that aims to be the gym standard for Multi-Agent Reinforcement Learning. We list the below advantages that make it suitable for use with flatland
+
+- Works with both rllib (https://docs.ray.io/en/latest/rllib.html) and stable baselines 3 (https://stable-baselines3.readthedocs.io/) using wrappers from Super Suit.
+- Clean API (https://www.pettingzoo.ml/api) with additional facilities/api for parallel, saving observation, recording using gym monitor, processing, normalising observations
+- Scikit-learn inspired api
+ e.g.
+
+```python
+act = model.predict(obs, deterministic=True)[0]
+```
+
+- Parallel learning using literally 2 lines of code to use with stable baselines 3
+
+```python
+env = ss.pettingzoo_env_to_vec_env_v0(env)
+env = ss.concat_vec_envs_v0(env, 8, num_cpus=4, base_class=’stable_baselines3’)
+```
+
+- Tested and supports various multi-agent environments with many agents comparable to flatland. e.g. https://www.pettingzoo.ml/magent
+- Clean interface means we can custom add an experimenting tool like wandb and have full flexibility to save information we want
diff --git a/docs/interface/pettingzoo.rst b/docs/interface/pettingzoo.rst
new file mode 100644
index 0000000000000000000000000000000000000000..35250de2ee8006bafe7414a87b86685481bc9161
--- /dev/null
+++ b/docs/interface/pettingzoo.rst
@@ -0,0 +1,35 @@
+
+PettingZoo
+==========
+
+..
+
+ PettingZoo (https://www.pettingzoo.ml/) is a collection of multi-agent environments for reinforcement learning. We build a pettingzoo interface for flatland.
+
+
+Background
+----------
+
+PettingZoo is a popular multi-agent environment library (https://arxiv.org/abs/2009.14471) that aims to be the gym standard for Multi-Agent Reinforcement Learning. We list the below advantages that make it suitable for use with flatland
+
+
+* Works with both rllib (https://docs.ray.io/en/latest/rllib.html) and stable baselines 3 (https://stable-baselines3.readthedocs.io/) using wrappers from Super Suit.
+* Clean API (https://www.pettingzoo.ml/api) with additional facilities/api for parallel, saving observation, recording using gym monitor, processing, normalising observations
+* Scikit-learn inspired api
+ e.g.
+
+.. code-block:: python
+
+ act = model.predict(obs, deterministic=True)[0]
+
+
+* Parallel learning using literally 2 lines of code to use with stable baselines 3
+
+.. code-block:: python
+
+ env = ss.pettingzoo_env_to_vec_env_v0(env)
+ env = ss.concat_vec_envs_v0(env, 8, num_cpus=4, base_class=’stable_baselines3’)
+
+
+* Tested and supports various multi-agent environments with many agents comparable to flatland. e.g. https://www.pettingzoo.ml/magent
+* Clean interface means we can custom add an experimenting tool like wandb and have full flexibility to save information we want
diff --git a/docs/interface/wrappers.md b/docs/interface/wrappers.md
new file mode 100644
index 0000000000000000000000000000000000000000..f853ee597feb103431d639797e54c402d3c1c5e5
--- /dev/null
+++ b/docs/interface/wrappers.md
@@ -0,0 +1,31 @@
+# Environment Wrappers
+
+> We provide various environment wrappers to work with both the rail env and the petting zoo interface.
+
+## Background
+
+These wrappers changes certain environment behavior which can help to get better reinforcement learning training.
+
+## Supported Inbuilt Wrappers
+
+We provide 2 sample wrappers for ShortestPathAction wrapper and SkipNoChoice wrapper. The wrappers requires many env properties that are only created on environment reset. Hence before using the wrapper, we must reset the rail env. To use the wrappers, simply pass the resetted rail env. Code samples are shown below for each wrapper.
+
+### ShortestPathAction Wrapper
+
+To use the ShortestPathAction Wrapper, simply wrap the rail env as follows
+
+```python
+rail_env.reset(random_seed=1)
+rail_env = ShortestPathActionWrapper(rail_env)
+```
+
+The shortest path action wrapper maps the existing action space into 3 actions - Shortest Path (`0`), Next Shortest Path (`1`) and Stop (`2`). Hence, we must ensure that the predicted action should always be one of these (0, 1 and 2) actions. To route all agents in the shortest path, pass `0` as the action.
+
+### SkipNoChoice Wrapper
+
+To use the SkipNoChoiceWrapper, simply wrap the rail env as follows
+
+```python
+rail_env.reset(random_seed=1)
+rail_env = SkipNoChoiceCellsWrapper(rail_env, accumulate_skipped_rewards=False, discounting=0.0)
+```
diff --git a/docs/interface/wrappers.rst b/docs/interface/wrappers.rst
new file mode 100644
index 0000000000000000000000000000000000000000..3eec73420ebb0437853622f3650943b96d19d064
--- /dev/null
+++ b/docs/interface/wrappers.rst
@@ -0,0 +1,40 @@
+
+Environment Wrappers
+====================
+
+..
+
+ We provide various environment wrappers to work with both the rail env and the petting zoo interface.
+
+
+Background
+----------
+
+These wrappers changes certain environment behavior which can help to get better reinforcement learning training.
+
+Supported Inbuilt Wrappers
+--------------------------
+
+We provide 2 sample wrappers for ShortestPathAction wrapper and SkipNoChoice wrapper. The wrappers requires many env properties that are only created on environment reset. Hence before using the wrapper, we must reset the rail env. To use the wrappers, simply pass the resetted rail env. Code samples are shown below for each wrapper.
+
+ShortestPathAction Wrapper
+^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+To use the ShortestPathAction Wrapper, simply wrap the rail env as follows
+
+.. code-block:: python
+
+ rail_env.reset(random_seed=1)
+ rail_env = ShortestPathActionWrapper(rail_env)
+
+The shortest path action wrapper maps the existing action space into 3 actions - Shortest Path (\ ``0``\ ), Next Shortest Path (\ ``1``\ ) and Stop (\ ``2``\ ). Hence, we must ensure that the predicted action should always be one of these (0, 1 and 2) actions. To route all agents in the shortest path, pass ``0`` as the action.
+
+SkipNoChoice Wrapper
+^^^^^^^^^^^^^^^^^^^^
+
+To use the SkipNoChoiceWrapper, simply wrap the rail env as follows
+
+.. code-block:: python
+
+ rail_env.reset(random_seed=1)
+ rail_env = SkipNoChoiceCellsWrapper(rail_env, accumulate_skipped_rewards=False, discounting=0.0)
diff --git a/make_docs.py b/make_docs.py
index 05b99111ce7007b164ab1442402e585f91098b87..adbab9da62f042b503f074d409cde1a695399578 100644
--- a/make_docs.py
+++ b/make_docs.py
@@ -24,7 +24,7 @@ for image_file in glob.glob(r'./docs/flatland*.rst'):
remove_exists(image_file)
remove_exists('docs/modules.rst')
-for md_file in glob.glob(r'./*.md') + glob.glob(r'./docs/specifications/*.md') + glob.glob(r'./docs/tutorials/*.md'):
+for md_file in glob.glob(r'./*.md') + glob.glob(r'./docs/specifications/*.md') + glob.glob(r'./docs/tutorials/*.md') + glob.glob(r'./docs/interface/*.md'):
from m2r import parse_from_file
rst_content = parse_from_file(md_file)