# Match-3 with ML-Agents ## Getting started The C# code for Match-3 exists inside of the Unity package (`com.unity.ml-agents`). The good first 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. ## Technical specifications for Match-3 with ML-Agents ### AbstractBoard class The `AbstractBoard` is the bridge between ML-Agents and your game. It allows ML-Agents to * ask your game what the current and maximum sizes (rows, columns, and potential piece types) of the board are * ask your game what the "color" of a cell is * ask whether the cell is a "special" piece type or not * ask your game whether a move is allowed * request that your game make a move These are handled by implementing the abstract methods of `AbstractBoard`. ##### `public abstract BoardSize GetMaxBoardSize()` Returns the largest `BoardSize` that the game can use. This is used to determine the sizes of observations and sensors, so don't make it larger than necessary. ##### `public virtual BoardSize GetCurrentBoardSize()` Returns the current size of the board. Each field on this BoardSize must be less than or equal to the corresponding field returned by `GetMaxBoardSize()`. This method is optional; if your always use the same size board, you don't need to override it. If the current board size is smaller than the maximum board size, `GetCellType()` and `GetSpecialType()` will not be called for cells outside the current board size, and `IsValidMove` won't be called for moves that would go outside of the current board size. ##### `public abstract int GetCellType(int row, int col)` Returns the "color" of piece at the given row and column. This should be between 0 and BoardSize.NumCellTypes-1 (inclusive). The actual order of the values doesn't matter. ##### `public abstract int GetSpecialType(int row, int col)` Returns the special type of the piece at the given row and column. This should be between 0 and BoardSize.NumSpecialTypes (inclusive). The actual order of the values doesn't matter. ##### `public abstract bool IsMoveValid(Move m)` Check whether the particular `Move` is valid for the game. The actual results will depend on the rules of the game, but we provide the `SimpleIsMoveValid()` method that handles basic match3 rules with no special or immovable pieces. ##### `public abstract bool MakeMove(Move m)` Instruct the game to make the given move. Returns true if the move was made. Note that during training, a move that was marked as invalid may occasionally still be requested. If this happens, it is safe to do nothing and request another move. ### `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(maxBoardSize)`. There are two helper functions to create a new `Move`: * `public static Move FromMoveIndex(int moveIndex, BoardSize maxBoardSize)` can be used to iterate over all potential moves for the board by looping from 0 to `Move.NumPotentialMoves()` * `public static Move FromPositionAndDirection(int row, int col, Direction dir, BoardSize maxBoardSize)` creates a `Move` from a row, column, and direction (and board size). ### `BoardSize` struct Describes the "size" of the board, including the number of potential piece types that the board can have. This is returned by the AbstractBoard.GetMaxBoardSize() and GetCurrentBoardSize() methods. #### `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. A `Match3SensorComponent` generates `Match3Sensor`s (the exact number of sensors depends on your configuration) at runtime, and should be added to the same GameObject as your `Agent` implementation. You do not need to write any additional code to use them. #### `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. A `Match3ActuatorComponent` generates a `Match3Actuator` at runtime, and should be added to the same GameObject as your `Agent` implementation. You do not need to write any additional code to use them. ### 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). * Add the `Agent`, `AbstractBoard` implementation, `Match3SensorComponent`, and `Match3ActuatorComponent` to the same `GameObject`. * Call `Agent.RequestDecision()` when you're ready for the `Agent` to make a move on the next `Academy` step. During the next `Academy` step, the `MakeMove()` method on the board will be called. ## Implementation Details ### Action Space The indexing for actions is the same as described in [Human Like Playtesting with Deep Learning](https://www.researchgate.net/publication/328307928_Human-Like_Playtesting_with_Deep_Learning) (for example, Figure 2b). The horizontal moves are enumerated first, then the vertical ones. ## Feedback 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.