Add a section on exporting to TFLite/Coral with demonstration (#679)

* Add a section on exporting to TFLite/Coral with demonstration

* Changelog to reflect new export documentation

* Update docs/guide/export.rst

Fingers on autopilot make word wrong

Co-authored-by: Anssi <kaneran21@hotmail.com>

* Update docs/guide/export.rst

Better wording clarity

Co-authored-by: Anssi <kaneran21@hotmail.com>

* Update docs/guide/export.rst

Better wording clarity

Co-authored-by: Anssi <kaneran21@hotmail.com>

* Clarify motivations and hardware

* Update docs/misc/changelog.rst

Make consistent with other changelog entries

Co-authored-by: Anssi <kaneran21@hotmail.com>

* Sphinx wants the section underline to be at least this long

* Remove first-person voice

* Typos

Co-authored-by: Anssi <kaneran21@hotmail.com>

docs/guide/export.rst

@@ -33,7 +33,7 @@ Export to ONNX
 
 As of June 2021, ONNX format  `doesn't support <https://github.com/onnx/onnx/issues/3033>`_ exporting models that use the ``broadcast_tensors`` functionality of pytorch. So in order to export the trained stable-baseline3 models in the ONNX format, we need to first remove the layers that use broadcasting. This can be done by creating a class that removes the unsupported layers.
 
-The following examples are for ``MlpPolicy`` only, and are general examples. Note that you have to preprocess the observation the same way stable-baselines3 agent does (see ``common.preprocessing.preprocess_obs``)
+The following examples are for ``MlpPolicy`` only, and are general examples. Note that you have to preprocess the observation the same way stable-baselines3 agent does (see ``common.preprocessing.preprocess_obs``).
 
 For PPO, assuming a shared feature extactor.
 
@@ -127,6 +127,38 @@ TODO: contributors help is welcomed!
 Probably a good starting point: https://github.com/elliotwaite/pytorch-to-javascript-with-onnx-js
 
 
+Export to TFLite / Coral (Edge TPU)
+-----------------------------------
+
+Full example code: https://github.com/chunky/sb3_to_coral
+
+Google created a chip called the "Coral" for deploying AI to the
+edge. It's available in a variety of form factors, including USB (using
+the Coral on a Rasbperry pi, with a SB3-developed model, was the original
+motivation for the code example above).
+
+The Coral chip is fast, with very low power consumption, but only has limited
+on-device training abilities. More information is on the webpage here:
+https://coral.ai.
+
+To deploy to a Coral, one must work via TFLite, and quantise the
+network to reflect the Coral's capabilities. The full chain to go from
+SB3 to Coral is: SB3 (Torch) => ONNX => TensorFlow => TFLite => Coral.
+
+The code linked above is a complete, minimal, example that:
+
+1. Creates a model using SB3
+2. Follows the path of exports all the way to TFLite and Google Coral
+3. Demonstrates the forward pass for most exported variants
+
+There are a number of pitfalls along the way to the complete conversion
+that this example covers, including:
+
+- Making the Gym's observation work with ONNX properly
+- Quantising the TFLite model appropriately to align with Gym
+  while still taking advantage of Coral
+- Using OnnxablePolicy described as described in the above example
+
 
 Manual export
 -------------

docs/misc/changelog.rst

@@ -35,6 +35,7 @@ Documentation:
 - Add tactile-gym to projects page (@ac-93)
 - Fix indentation in the RL tips page (@cove9988)
 - Update GAE computation docstring
+- Add documentation on exporting to TFLite/Coral
 
 
 Release 1.3.0 (2021-10-23)