Release 10 match3 doc (#4647)

* Change title

* added overvierw, cleaned up structure.

* Updated with getting started, clean up

* added link to form.  added note about playable

* clean up

* Update Match3.md

com.unity.ml-agents.extensions/Documentation~/Match3.md

-# Match-3 Game Support
+# Match-3 with ML-Agents
+
+<img src="images/match3.png" align="center" width="3000"/>
+
+## Overview
+One of the main feedback we get is to illustrate more real game examples using ML-Agents. We are excited to provide an example implementation of Match-3 using ML-Agents and additional utilities to integrate ML-Agents with Match-3 games.
+
+Our aim is to enable Match-3 teams to leverage ML-Agents to create player agents to learn and play different Match-3 levels. This implementation is intended as a starting point and guide for teams to get started (as there are many nuances with Match-3 for training ML-Agents) and for us to iterate both on the C#, hyperparameters, and trainers to improve ML-Agents for Match-3.
+
+This implementation includes:
+
+* C# implementation catered toward a Match-3 setup including concepts around encoding for moves based on [Human Like Playtesting with Deep Learning](https://www.researchgate.net/publication/328307928_Human-Like_Playtesting_with_Deep_Learning)
+* An example Match-3 scene with ML-Agents implemented (located under /Project/Assets/ML-Agents/Examples/Match3)
+
+If you are a Match-3 developer and are trying to leverage ML-Agents for this scenario, [we want to hear from you](https://forms.gle/TBsB9jc8WshgzViU9). Additionally, we are also looking for interested Match-3 teams to speak with us for 45 minutes. If you are interested, please indicate that in the [form](https://forms.gle/TBsB9jc8WshgzViU9).  If selected, we will provide gift cards as a token of appreciation.
+
+## Interested in more game templates?
+Do you have a type of game you are interested for ML-Agents?  If so, please post a [forum issue](https://forum.unity.com/forums/ml-agents.453/) with [GAME TEMPLATE] in the title.
+
+## Getting started
+The C# code for Match-3 exists inside of the extensions package (com.unity.ml-agents.extensions).  A good first step would be to familiarize with the extensions package by reading the document [here](com.unity.ml-agents.extensions.md).  The second step would be to take a look at how we have implemented the C# code in the example Match-3 scene (located under /Project/Assets/ML-Agents/Examples/match3).  Once you have some familiarity, then the next step would be to implement the C# code for Match-3 from the extensions package.
+
+Additionally, see below for additional technical specifications on the C# code for Match-3. Please note the Match-3 game isn't human playable as implemented and can be only played via training.
-We provide some utilities to integrate ML-Agents with Match-3 games.
-<img src="images/match3.png" align="middle" width="3000"/>
+## Technical specifications for Match-3 with ML-Agents
-## AbstractBoard class
+### AbstractBoard class
 The `AbstractBoard` is the bridge between ML-Agents and your game. It allows ML-Agents to
 * ask your game what the "color" of a cell is
 * ask whether the cell is a "special" piece type or not
 
 The AbstractBoard also tracks the number of rows, columns, and potential piece types that the board can have.
 
-#### `public abstract int GetCellType(int row, int col)`
+##### `public abstract int GetCellType(int row, int col)`
-#### `public abstract int GetSpecialType(int row, int col)`
+##### `public abstract int GetSpecialType(int row, int col)`
-#### `public abstract bool IsMoveValid(Move m)`
+##### `public abstract bool IsMoveValid(Move m)`
-#### `public abstract bool MakeMove(Move m)`
+##### `public abstract bool MakeMove(Move m)`
-## Move struct
+### Move struct
 The Move struct encapsulates a swap of two adjacent cells. You can get the number of potential moves
 for a board of a given size with. `Move.NumPotentialMoves(NumRows, NumColumns)`. There are two helper
 functions to create a new `Move`:
 a `Move` from a row, column, and direction (and board size).
 
-## `Match3Sensor` and `Match3SensorComponent` classes
+#### `Match3Sensor` and `Match3SensorComponent` classes
 The `Match3Sensor` generates observations about the state using the `AbstractBoard` interface. You can
 choose whether to use vector or "visual" observations; in theory, visual observations should perform
 better because they are 2-dimensional like the board, but we need to experiment more on this.
 
-## `Match3Actuator` and `Match3ActuatorComponent` classes
+#### `Match3Actuator` and `Match3ActuatorComponent` classes
 The `Match3Actuator` converts actions from training or inference into a `Move` that is sent to` AbstractBoard.MakeMove()`
 It also checks `AbstractBoard.IsMoveValid` for each potential move and uses this to set the action mask for Agent.
 
-# Setting up match-3 simulation
+### Setting up Match-3 simulation
 * Implement the `AbstractBoard` methods to integrate with your game.
 * Give the `Agent` rewards when it does what you want it to (match multiple pieces in a row, clears pieces of a certain
 type, etc).