[MLA-1584] Match3 variable board size (#5189)

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Project/Assets/ML-Agents/Examples/Match3/Scenes/Match3.unity

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Project/Assets/ML-Agents/Examples/Match3/Scripts/Match3Agent.cs

         {
             base.OnEpisodeBegin();
 
+            Board.UpdateCurrentBoardSize();
             Board.InitSettled();
             m_CurrentState = State.FindMatches;
             m_TimeUntilMove = MoveTime;

Project/Assets/ML-Agents/Examples/Match3/Scripts/Match3Board.cs

 using System;
 using Unity.MLAgents.Extensions.Match3;
 using UnityEngine;
+using UnityEngine.Serialization;
 
 namespace Unity.MLAgentsExamples
 {
     {
+        public int MinRows;
+        [FormerlySerializedAs("Rows")]
+        public int MaxRows;
+
+        public int MinColumns;
+        [FormerlySerializedAs("Columns")]
+        public int MaxColumns;
+
+        public int NumCellTypes;
+        public int NumSpecialTypes;
+
         public const int k_EmptyCell = -1;
         [Tooltip("Points earned for clearing a basic cell (cube)")]
         public int BasicCellPoints = 1;
         /// </summary>
         public int RandomSeed;
 
-        (int, int)[,] m_Cells;
+        (int CellType, int SpecialType)[,] m_Cells;
+        private BoardSize m_CurrentBoardSize;
+
-            m_Cells = new (int, int)[Columns, Rows];
-            m_Matched = new bool[Columns, Rows];
+            m_Cells = new (int, int)[MaxColumns, MaxRows];
+            m_Matched = new bool[MaxColumns, MaxRows];
+            // Start using the max rows and columns, but we'll update the current size at the start of each episode.
+            m_CurrentBoardSize = new BoardSize
+            {
+                Rows = MaxRows,
+                Columns = MaxColumns,
+                NumCellTypes = NumCellTypes,
+                NumSpecialTypes = NumSpecialTypes
+            };
         }
 
         void Start()
         }
 
+        public override BoardSize GetMaxBoardSize()
+        {
+            return new BoardSize
+            {
+                Rows = MaxRows,
+                Columns = MaxColumns,
+                NumCellTypes = NumCellTypes,
+                NumSpecialTypes = NumSpecialTypes
+            };
+        }
+
+        public override BoardSize GetCurrentBoardSize()
+        {
+            return m_CurrentBoardSize;
+        }
+
+        /// <summary>
+        /// Change the board size to a random size between the min and max rows and columns. This is
+        /// cached so that the size is consistent until it is updated.
+        /// This is just for an example; you can change your board size however you want.
+        /// </summary>
+        public void UpdateCurrentBoardSize()
+        {
+            var newRows = m_Random.Next(MinRows, MaxRows + 1);
+            var newCols = m_Random.Next(MinColumns, MaxColumns + 1);
+            m_CurrentBoardSize.Rows = newRows;
+            m_CurrentBoardSize.Columns = newCols;
+        }
+
         public override bool MakeMove(Move move)
         {
             if (!IsMoveValid(move))
 
         public override int GetCellType(int row, int col)
         {
-            return m_Cells[col, row].Item1;
+            if (row >= m_CurrentBoardSize.Rows || col >= m_CurrentBoardSize.Columns)
+            {
+                throw new IndexOutOfRangeException();
+            }
+            return m_Cells[col, row].CellType;
-            return m_Cells[col, row].Item2;
+            if (row >= m_CurrentBoardSize.Rows || col >= m_CurrentBoardSize.Columns)
+            {
+                throw new IndexOutOfRangeException();
+            }
+            return m_Cells[col, row].SpecialType;
         }
 
         public override bool IsMoveValid(Move m)
         {
             ClearMarked();
             bool madeMatch = false;
-            for (var i = 0; i < Rows; i++)
+            for (var i = 0; i < m_CurrentBoardSize.Rows; i++)
-                for (var j = 0; j < Columns; j++)
+                for (var j = 0; j < m_CurrentBoardSize.Columns; j++)
-                    for (var iOffset = i; iOffset < Rows; iOffset++)
+                    for (var iOffset = i; iOffset < m_CurrentBoardSize.Rows; iOffset++)
-                        if (m_Cells[j, i].Item1 != m_Cells[j, iOffset].Item1)
+                        if (m_Cells[j, i].CellType != m_Cells[j, iOffset].CellType)
                         {
                             break;
                         }
 
                     // Check vertically
                     var matchedCols = 0;
-                    for (var jOffset = j; jOffset < Columns; jOffset++)
+                    for (var jOffset = j; jOffset < m_CurrentBoardSize.Columns; jOffset++)
-                        if (m_Cells[j, i].Item1 != m_Cells[jOffset, i].Item1)
+                        if (m_Cells[j, i].CellType != m_Cells[jOffset, i].CellType)
                         {
                             break;
                         }
         {
             var pointsByType = new[] { BasicCellPoints, SpecialCell1Points, SpecialCell2Points };
             int pointsEarned = 0;
-            for (var i = 0; i < Rows; i++)
+            for (var i = 0; i < m_CurrentBoardSize.Rows; i++)
-                for (var j = 0; j < Columns; j++)
+                for (var j = 0; j < m_CurrentBoardSize.Columns; j++)
                 {
                     if (m_Matched[j, i])
                     {
         {
             var madeChanges = false;
             // Gravity is applied in the negative row direction
-            for (var j = 0; j < Columns; j++)
+            for (var j = 0; j < m_CurrentBoardSize.Columns; j++)
-                for (var readIndex = 0; readIndex < Rows; readIndex++)
+                for (var readIndex = 0; readIndex < m_CurrentBoardSize.Rows; readIndex++)
-                    if (m_Cells[j, readIndex].Item1 != k_EmptyCell)
+                    if (m_Cells[j, readIndex].CellType != k_EmptyCell)
                     {
                         writeIndex++;
                     }
-                for (; writeIndex < Rows; writeIndex++)
+                for (; writeIndex < m_CurrentBoardSize.Rows; writeIndex++)
                 {
                     madeChanges = true;
                     m_Cells[j, writeIndex] = (k_EmptyCell, 0);
         public bool FillFromAbove()
         {
             bool madeChanges = false;
-            for (var i = 0; i < Rows; i++)
+            for (var i = 0; i < m_CurrentBoardSize.Rows; i++)
-                for (var j = 0; j < Columns; j++)
+                for (var j = 0; j < m_CurrentBoardSize.Columns; j++)
-                    if (m_Cells[j, i].Item1 == k_EmptyCell)
+                    if (m_Cells[j, i].CellType == k_EmptyCell)
                     {
                         madeChanges = true;
                         m_Cells[j, i] = (GetRandomCellType(), GetRandomSpecialType());
         // Initialize the board to random values.
         public void InitRandom()
         {
-            for (var i = 0; i < Rows; i++)
+            for (var i = 0; i < MaxRows; i++)
-                for (var j = 0; j < Columns; j++)
+                for (var j = 0; j < MaxColumns; j++)
                 {
                     m_Cells[j, i] = (GetRandomCellType(), GetRandomSpecialType());
                 }
 
         void ClearMarked()
         {
-            for (var i = 0; i < Rows; i++)
+            for (var i = 0; i < MaxRows; i++)
-                for (var j = 0; j < Columns; j++)
+                for (var j = 0; j < MaxColumns; j++)
                 {
                     m_Matched[j, i] = false;
                 }

Project/Assets/ML-Agents/Examples/Match3/Scripts/Match3Drawer.cs

 using System.Collections.Generic;
 using UnityEngine;
+using UnityEngine.Profiling;
 
 namespace Unity.MLAgentsExamples
 {
 
             tilesDict.Clear();
 
-            for (var i = 0; i < m_Board.Rows; i++)
+            for (var i = 0; i < m_Board.MaxRows; i++)
-                for (var j = 0; j < m_Board.Columns; j++)
+                for (var j = 0; j < m_Board.MaxColumns; j++)
                 {
                     var go = Instantiate(TilePrefab, transform.position, Quaternion.identity, transform);
                     go.name = $"r{i}_c{j}";
                 InitializeDict();
             }
 
-            for (var i = 0; i < m_Board.Rows; i++)
+            var currentSize = m_Board.GetCurrentBoardSize();
+            for (var i = 0; i < m_Board.MaxRows; i++)
-                for (var j = 0; j < m_Board.Columns; j++)
+                for (var j = 0; j < m_Board.MaxColumns; j++)
-                    var value = m_Board.Cells != null ? m_Board.GetCellType(i, j) : Match3Board.k_EmptyCell;
+                    int value = Match3Board.k_EmptyCell;
+                    int specialType = 0;
+                    if (m_Board.Cells != null && i < currentSize.Rows && j < currentSize.Columns)
+                    {
+                        value = m_Board.GetCellType(i, j);
+                        specialType = m_Board.GetSpecialType(i, j);
+                    }
-                    var specialType = m_Board.Cells != null ? m_Board.GetSpecialType(i, j) : 0;
                     tilesDict[(i, j)].transform.position = transform.TransformPoint(pos);
                     tilesDict[(i, j)].SetActiveTile(specialType, value);
                 }
         void OnDrawGizmos()
         {
+            Profiler.BeginSample("Match3.OnDrawGizmos");
             var cubeSize = .5f;
             var matchedWireframeSize = .5f * (cubeSize + CubeSpacing);
 
             }
 
-            for (var i = 0; i < m_Board.Rows; i++)
+            var currentSize = m_Board.GetCurrentBoardSize();
+            for (var i = 0; i < m_Board.MaxRows; i++)
-                for (var j = 0; j < m_Board.Columns; j++)
+                for (var j = 0; j < m_Board.MaxColumns; j++)
-                    var value = m_Board.Cells != null ? m_Board.GetCellType(i, j) : Match3Board.k_EmptyCell;
+                    int value = Match3Board.k_EmptyCell;
+                    int specialType = 0;
+                    if (m_Board.Cells != null && i < currentSize.Rows && j < currentSize.Columns)
+                    {
+                        value = m_Board.GetCellType(i, j);
+                        specialType = m_Board.GetSpecialType(i, j);
+                    }
+
                     if (value >= 0 && value < s_Colors.Length)
                     {
                         Gizmos.color = s_Colors[value];
                     var pos = new Vector3(j, i, 0);
                     pos *= CubeSpacing;
 
-                    var specialType = m_Board.Cells != null ? m_Board.GetSpecialType(i, j) : 0;
                     if (specialType == 2)
                     {
                         Gizmos.DrawCube(transform.TransformPoint(pos), cubeSize * new Vector3(1f, .5f, .5f));
                 var threeQuarters = Vector3.Lerp(pos, otherPos, .75f);
                 Gizmos.DrawLine(transform.TransformPoint(oneQuarter), transform.TransformPoint(threeQuarters));
             }
+
+            Profiler.EndSample();
         }
     }
 }

Project/Assets/ML-Agents/Examples/Match3/Scripts/Match3ExampleActuator.cs

 {
     public class Match3ExampleActuator : Match3Actuator
     {
-        Match3Board Board => (Match3Board)m_Board;
+        private Match3Board m_Board;
+        Match3Board Board => m_Board;
-            Agent agent,
-            )
-            : base(board, forceHeuristic, seed, agent, name) { }
+        )
+            : base(board, forceHeuristic, seed, name)
+        {
+            m_Board = board;
+        }
 
 
         protected override int EvalMovePoints(Move move)
-            var moveVal = m_Board.GetCellType(move.Row, move.Column);
-            var moveSpecial = m_Board.GetSpecialType(move.Row, move.Column);
+            var moveVal = Board.GetCellType(move.Row, move.Column);
+            var moveSpecial = Board.GetSpecialType(move.Row, move.Column);
-            var oppositeVal = m_Board.GetCellType(otherRow, otherCol);
-            var oppositeSpecial = m_Board.GetSpecialType(otherRow, otherCol);
+            var oppositeVal = Board.GetCellType(otherRow, otherCol);
+            var oppositeSpecial = Board.GetSpecialType(otherRow, otherCol);
 
 
             int movePoints = EvalHalfMove(
         int EvalHalfMove(int newRow, int newCol, int newValue, int newSpecial, Direction incomingDirection, int[] pointsByType)
         {
             // This is a essentially a duplicate of AbstractBoard.CheckHalfMove but also counts the points for the move.
+            var currentBoardSize = Board.GetCurrentBoardSize();
             int matchedLeft = 0, matchedRight = 0, matchedUp = 0, matchedDown = 0;
             int scoreLeft = 0, scoreRight = 0, scoreUp = 0, scoreDown = 0;
 
                 {
-                    if (m_Board.GetCellType(newRow, c) == newValue)
+                    if (Board.GetCellType(newRow, c) == newValue)
-                        scoreLeft += pointsByType[m_Board.GetSpecialType(newRow, c)];
+                        scoreLeft += pointsByType[Board.GetSpecialType(newRow, c)];
                     }
                     else
                         break;
             if (incomingDirection != Direction.Left)
             {
-                for (var c = newCol + 1; c < m_Board.Columns; c++)
+                for (var c = newCol + 1; c < currentBoardSize.Columns; c++)
-                    if (m_Board.GetCellType(newRow, c) == newValue)
+                    if (Board.GetCellType(newRow, c) == newValue)
-                        scoreRight += pointsByType[m_Board.GetSpecialType(newRow, c)];
+                        scoreRight += pointsByType[Board.GetSpecialType(newRow, c)];
                     }
                     else
                         break;
             if (incomingDirection != Direction.Down)
             {
-                for (var r = newRow + 1; r < m_Board.Rows; r++)
+                for (var r = newRow + 1; r < currentBoardSize.Rows; r++)
-                    if (m_Board.GetCellType(r, newCol) == newValue)
+                    if (Board.GetCellType(r, newCol) == newValue)
-                        scoreUp += pointsByType[m_Board.GetSpecialType(r, newCol)];
+                        scoreUp += pointsByType[Board.GetSpecialType(r, newCol)];
                     }
                     else
                         break;
             {
                 for (var r = newRow - 1; r >= 0; r--)
                 {
-                    if (m_Board.GetCellType(r, newCol) == newValue)
+                    if (Board.GetCellType(r, newCol) == newValue)
-                        scoreDown += pointsByType[m_Board.GetSpecialType(r, newCol)];
+                        scoreDown += pointsByType[Board.GetSpecialType(r, newCol)];
                     }
                     else
                         break;

Project/Assets/ML-Agents/Examples/Match3/Scripts/Match3ExampleActuatorComponent.cs

             var board = GetComponent<Match3Board>();
             var agent = GetComponentInParent<Agent>();
             var seed = RandomSeed == -1 ? gameObject.GetInstanceID() : RandomSeed + 1;
-            return new IActuator[] { new Match3ExampleActuator(board, ForceHeuristic, agent, ActuatorName, seed) };
+            return new IActuator[] { new Match3ExampleActuator(board, ForceHeuristic, ActuatorName, seed) };
         }
     }
 }

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

 <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.
+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.
+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.
-* 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). More information, on Match-3 example [here](https://github.com/Unity-Technologies/ml-agents/tree/release_15_docs/docs/docs/Learning-Environment-Examples.md#match-3).
+* 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).
+More information on the Match-3 example is [here](https://github.com/Unity-Technologies/ml-agents/tree/release_15_docs/docs/docs/Learning-Environment-Examples.md#match-3).
-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.
+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.
-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.
+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.
-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.
+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.
+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.
+* ask your game what the current and maximum sizes (rows, columns, and potential piece types) of the board are
-These are handled by implementing the `GetCellType()`, `IsMoveValid()`, and `MakeMove()` abstract methods.
+These are handled by implementing the abstract methods of `AbstractBoard`.
-The AbstractBoard also tracks the number of rows, columns, and potential piece types that the board can have.
+##### `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.
 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
+### `Move` struct
-for a board of a given size with. `Move.NumPotentialMoves(NumRows, NumColumns)`. There are two helper
+for a board of a given size with. `Move.NumPotentialMoves(maxBoardSize)`. There are two helper
-* `public static Move FromMoveIndex(int moveIndex, int maxRows, int maxCols)` can be used to
+* `public static Move FromMoveIndex(int moveIndex, BoardSize maxBoardSize)` can be used to
-* `public static Move FromPositionAndDirection(int row, int col, Direction dir, int maxRows, int maxCols)` creates
+* `public static Move FromPositionAndDirection(int row, int col, Direction dir, BoardSize maxBoardSize)` creates
+
+### `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
-A `Match3SensorComponent` generates a `Match3Sensor` 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.
+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()`
 `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.
+<img src="images/match3-moves.png" align="center"/>

com.unity.ml-agents.extensions/Runtime/Match3/AbstractBoard.cs

 using System;
 using System.Collections.Generic;
+using System.Diagnostics;
+using Debug = UnityEngine.Debug;
-    public abstract class AbstractBoard : MonoBehaviour
+    /// <summary>
+    /// Representation of the AbstractBoard size and number of cell and special types.
+    /// </summary>
+    public struct BoardSize
     {
         /// <summary>
         /// Number of rows on the board
         public int NumSpecialTypes;
 
         /// <summary>
+        /// Check that all fields of the left-hand BoardSize are less than or equal to the field of the right-hand BoardSize
+        /// </summary>
+        /// <param name="lhs"></param>
+        /// <param name="rhs"></param>
+        /// <returns>True if all fields are less than or equal.</returns>
+        public static bool operator <=(BoardSize lhs, BoardSize rhs)
+        {
+            return lhs.Rows <= rhs.Rows && lhs.Columns <= rhs.Columns && lhs.NumCellTypes <= rhs.NumCellTypes &&
+                   lhs.NumSpecialTypes <= rhs.NumSpecialTypes;
+        }
+
+        /// <summary>
+        /// Check that all fields of the left-hand BoardSize are greater than or equal to the field of the right-hand BoardSize
+        /// </summary>
+        /// <param name="lhs"></param>
+        /// <param name="rhs"></param>
+        /// <returns>True if all fields are greater than or equal.</returns>
+        public static bool operator >=(BoardSize lhs, BoardSize rhs)
+        {
+            return lhs.Rows >= rhs.Rows && lhs.Columns >= rhs.Columns && lhs.NumCellTypes >= rhs.NumCellTypes &&
+                   lhs.NumSpecialTypes >= rhs.NumSpecialTypes;
+        }
+
+        /// <summary>
+        /// Return a string representation of the BoardSize.
+        /// </summary>
+        /// <returns></returns>
+        public override string ToString()
+        {
+            return
+                $"Rows: {Rows}, Columns: {Columns}, NumCellTypes: {NumCellTypes}, NumSpecialTypes: {NumSpecialTypes}";
+        }
+    }
+
+    /// <summary>
+    /// An adapter between ML Agents and a Match-3 game.
+    /// </summary>
+    public abstract class AbstractBoard : MonoBehaviour
+    {
+        /// <summary>
+        /// Return the maximum size of the board. This is used to determine the size of observations and actions,
+        /// so the returned values must not change.
+        /// </summary>
+        /// <returns></returns>
+        public abstract BoardSize GetMaxBoardSize();
+
+        /// <summary>
+        /// Return the current size of the board. The values must less than or equal to the values returned from
+        /// GetMaxBoardSize().
+        /// By default, this will return GetMaxBoardSize(); if your board doesn't change size, you don't need to
+        /// override it.
+        /// </summary>
+        /// <returns></returns>
+        public virtual BoardSize GetCurrentBoardSize()
+        {
+            return GetMaxBoardSize();
+        }
+
+        /// <summary>
         /// Returns the "color" of the piece at the given row and column.
         /// This should be between 0 and NumCellTypes-1 (inclusive).
         /// The actual order of the values doesn't matter.
         /// The actual results will depend on the rules of the game, but we provide SimpleIsMoveValid()
         /// that handles basic match3 rules with no special or immovable pieces.
         /// </summary>
+        /// <remarks>
+        /// Moves that would go outside of GetCurrentBoardSize() are filtered out before they are
+        /// passed to IsMoveValid().
+        /// </remarks>
         /// <param name="m"></param>
         /// <returns></returns>
         public abstract bool IsMoveValid(Move m);
         /// <returns></returns>
         public int NumMoves()
         {
-            return Move.NumPotentialMoves(Rows, Columns);
+            return Move.NumPotentialMoves(GetMaxBoardSize());
         }
 
         /// <summary>
         /// <returns></returns>
         public IEnumerable<Move> AllMoves()
         {
-            var currentMove = Move.FromMoveIndex(0, Rows, Columns);
-            for (var i = 0; i < NumMoves(); i++)
-            {
-                yield return currentMove;
-                currentMove.Next(Rows, Columns);
-            }
-        }
+            var maxBoardSize = GetMaxBoardSize();
+            var currentBoardSize = GetCurrentBoardSize();
-        /// <summary>
-        /// Iterate through all valid Moves on the board.
-        /// </summary>
-        /// <returns></returns>
-        public IEnumerable<Move> ValidMoves()
-        {
-            var currentMove = Move.FromMoveIndex(0, Rows, Columns);
+            var currentMove = Move.FromMoveIndex(0, maxBoardSize);
-                if (IsMoveValid(currentMove))
+                if (currentMove.InRangeForBoard(currentBoardSize))
-                currentMove.Next(Rows, Columns);
+                currentMove.Next(maxBoardSize);
-        /// Iterate through all invalid Moves on the board.
+        /// Iterate through all valid Moves on the board.
-        public IEnumerable<Move> InvalidMoves()
+        public IEnumerable<Move> ValidMoves()
-            var currentMove = Move.FromMoveIndex(0, Rows, Columns);
+            var maxBoardSize = GetMaxBoardSize();
+            var currentBoardSize = GetCurrentBoardSize();
+
+            var currentMove = Move.FromMoveIndex(0, maxBoardSize);
-                if (!IsMoveValid(currentMove))
+                if (currentMove.InRangeForBoard(currentBoardSize) && IsMoveValid(currentMove))
-                currentMove.Next(Rows, Columns);
+                currentMove.Next(maxBoardSize);
             }
         }
 
         /// <returns></returns>
         bool CheckHalfMove(int newRow, int newCol, int newValue, Direction incomingDirection)
         {
+            var currentBoardSize = GetCurrentBoardSize();
             int matchedLeft = 0, matchedRight = 0, matchedUp = 0, matchedDown = 0;
 
             if (incomingDirection != Direction.Right)
 
             if (incomingDirection != Direction.Left)
             {
-                for (var c = newCol + 1; c < Columns; c++)
+                for (var c = newCol + 1; c < currentBoardSize.Columns; c++)
                 {
                     if (GetCellType(newRow, c) == newValue)
                         matchedRight++;
 
             if (incomingDirection != Direction.Down)
             {
-                for (var r = newRow + 1; r < Rows; r++)
+                for (var r = newRow + 1; r < currentBoardSize.Rows; r++)
                 {
                     if (GetCellType(r, newCol) == newValue)
                         matchedUp++;
             return false;
         }
 
+        /// <summary>
+        /// Make sure that the current BoardSize isn't larger than the original value of GetMaxBoardSize().
+        /// If it is, log a warning.
+        /// </summary>
+        /// <param name="originalMaxBoardSize"></param>
+        [Conditional("DEBUG")]
+        internal void CheckBoardSizes(BoardSize originalMaxBoardSize)
+        {
+            var currentBoardSize = GetCurrentBoardSize();
+            if (!(currentBoardSize <= originalMaxBoardSize))
+            {
+                Debug.LogWarning(
+                    "Current BoardSize is larger than maximum board size was on initialization. This may cause unexpected results.\n" +
+                    $"Original GetMaxBoardSize() result: {originalMaxBoardSize}\n" +
+                    $"GetCurrentBoardSize() result: {currentBoardSize}"
+                );
+            }
+        }
     }
 }

com.unity.ml-agents.extensions/Runtime/Match3/Match3Actuator.cs

-using System.Collections.Generic;
-using UnityEngine;
+using Debug = UnityEngine.Debug;
 
 
 namespace Unity.MLAgents.Extensions.Match3
     /// in action masks, and applies the action to the board via AbstractBoard.MakeMove().
     /// </summary>
-    public class Match3Actuator : IActuator, IHeuristicProvider, IBuiltInActuator
+    public class Match3Actuator : IActuator, IBuiltInActuator
-        protected AbstractBoard m_Board;
-        protected System.Random m_Random;
-        private ActionSpec m_ActionSpec;
-        private bool m_ForceHeuristic;
-        private Agent m_Agent;
-
-        private int m_Rows;
-        private int m_Columns;
-        private int m_NumCellTypes;
+        AbstractBoard m_Board;
+        System.Random m_Random;
+        ActionSpec m_ActionSpec;
+        bool m_ForceHeuristic;
+        BoardSize m_MaxBoardSize;
 
         /// <summary>
         /// Create a Match3Actuator.
         public Match3Actuator(AbstractBoard board,
                               bool forceHeuristic,
                               int seed,
-                              Agent agent,