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Merge branch 'develop-hybrid-action-staging' into develop-hybrid-actions-singleton

/develop/actionmodel-csharp
Andrew Cohen 4 年前
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共有 78 个文件被更改,包括 6613 次插入34 次删除
  1. 10
      README.md
  2. 5
      com.unity.ml-agents.extensions/Documentation~/com.unity.ml-agents.extensions.md
  3. 7
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  4. 2
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  5. 4
      com.unity.ml-agents/Runtime/Academy.cs
  6. 2
      com.unity.ml-agents/Runtime/Actuators/IActionReceiver.cs
  7. 2
      com.unity.ml-agents/Runtime/Actuators/IDiscreteActionMask.cs
  8. 26
      com.unity.ml-agents/Runtime/Agent.cs
  9. 2
      com.unity.ml-agents/Runtime/Demonstrations/DemonstrationRecorder.cs
  10. 2
      com.unity.ml-agents/Runtime/DiscreteActionMasker.cs
  11. 2
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  12. 4
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  13. 6
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  14. 21
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  15. 2
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  16. 4
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  18. 8
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README.md


# Unity ML-Agents Toolkit
[![docs badge](https://img.shields.io/badge/docs-reference-blue.svg)](https://github.com/Unity-Technologies/ml-agents/tree/release_8_docs/docs/)
[![docs badge](https://img.shields.io/badge/docs-reference-blue.svg)](https://github.com/Unity-Technologies/ml-agents/tree/release_9_docs/docs/)
[![license badge](https://img.shields.io/badge/license-Apache--2.0-green.svg)](LICENSE)

## Releases & Documentation
**Our latest, stable release is `Release 8`. Click
[here](https://github.com/Unity-Technologies/ml-agents/tree/release_8_docs/docs/Readme.md)
**Our latest, stable release is `Release 9`. Click
[here](https://github.com/Unity-Technologies/ml-agents/tree/release_9_docs/docs/Readme.md)
to get started with the latest release of ML-Agents.**
The table below lists all our releases, including our `master` branch which is

| **Version** | **Release Date** | **Source** | **Documentation** | **Download** |
|:-------:|:------:|:-------------:|:-------:|:------------:|
| **master (unstable)** | -- | [source](https://github.com/Unity-Technologies/ml-agents/tree/master) | [docs](https://github.com/Unity-Technologies/ml-agents/tree/master/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/master.zip) |
| **Release 8** | **October 14, 2020** | **[source](https://github.com/Unity-Technologies/ml-agents/tree/release_8)** | **[docs](https://github.com/Unity-Technologies/ml-agents/tree/release_8_docs/docs/Readme.md)** | **[download](https://github.com/Unity-Technologies/ml-agents/archive/release_8.zip)** |
| **Release 9** | **November 4, 2020** | **[source](https://github.com/Unity-Technologies/ml-agents/tree/release_9)** | **[docs](https://github.com/Unity-Technologies/ml-agents/tree/release_9_docs/docs/Readme.md)** | **[download](https://github.com/Unity-Technologies/ml-agents/archive/release_9.zip)** |
| **Release 8** | October 14, 2020 | [source](https://github.com/Unity-Technologies/ml-agents/tree/release_8) | [docs](https://github.com/Unity-Technologies/ml-agents/tree/release_8_docs/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/release_8.zip) |
| **Release 2** | May 20, 2020 | [source](https://github.com/Unity-Technologies/ml-agents/tree/release_2) | [docs](https://github.com/Unity-Technologies/ml-agents/tree/release_2_docs/docs/Readme.md) | [download](https://github.com/Unity-Technologies/ml-agents/archive/release_2.zip) |
## Citation

5
com.unity.ml-agents.extensions/Documentation~/com.unity.ml-agents.extensions.md


| _Runtime_ | Contains core C# APIs for integrating ML-Agents into your Unity scene. |
| _Tests_ | Contains the unit tests for the package. |
The Runtime directory currently contains three features:
* [Match-3 sensor and actuator](Match3.md)
* [Grid-based sensor](Grid-Sensor.md)
* Physics-based sensors
## Installation
The ML-Agents Extensions package is not currently available in the Package Manager. There are two
recommended ways to install the package:

7
com.unity.ml-agents.extensions/Tests/Editor/Sensors/RigidBodySensorTests.cs


bool isOK = SensorHelper.CompareObservation(sensor, expected, out errorMessage);
Assert.IsTrue(isOK, errorMessage);
}
public static void CompareObservation(ISensor sensor, float[,,] expected)
{
string errorMessage;
bool isOK = SensorHelper.CompareObservation(sensor, expected, out errorMessage);
Assert.IsTrue(isOK, errorMessage);
}
}
public class RigidBodySensorTests

2
com.unity.ml-agents/Documentation~/com.unity.ml-agents.md


[unity ML-Agents Toolkit]: https://github.com/Unity-Technologies/ml-agents
[unity inference engine]: https://docs.unity3d.com/Packages/com.unity.barracuda@latest/index.html
[package manager documentation]: https://docs.unity3d.com/Manual/upm-ui-install.html
[installation instructions]: https://github.com/Unity-Technologies/ml-agents/blob/release_8_docs/docs/Installation.md
[installation instructions]: https://github.com/Unity-Technologies/ml-agents/blob/release_9_docs/docs/Installation.md
[github repository]: https://github.com/Unity-Technologies/ml-agents
[python package]: https://github.com/Unity-Technologies/ml-agents
[execution order of event functions]: https://docs.unity3d.com/Manual/ExecutionOrder.html

4
com.unity.ml-agents/Runtime/Academy.cs


* API. For more information on each of these entities, in addition to how to
* set-up a learning environment and train the behavior of characters in a
* Unity scene, please browse our documentation pages on GitHub:
* https://github.com/Unity-Technologies/ml-agents/tree/release_8_docs/docs/
* https://github.com/Unity-Technologies/ml-agents/tree/release_9_docs/docs/
*/
namespace Unity.MLAgents

/// fall back to inference or heuristic decisions. (You can also set agents to always use
/// inference or heuristics.)
/// </remarks>
[HelpURL("https://github.com/Unity-Technologies/ml-agents/tree/release_8_docs/" +
[HelpURL("https://github.com/Unity-Technologies/ml-agents/tree/release_9_docs/" +
"docs/Learning-Environment-Design.md")]
public class Academy : IDisposable
{

2
com.unity.ml-agents/Runtime/Actuators/IActionReceiver.cs


///
/// See [Agents - Actions] for more information on masking actions.
///
/// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_8_docs/docs/Learning-Environment-Design-Agents.md#actions
/// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_9_docs/docs/Learning-Environment-Design-Agents.md#actions
/// </remarks>
/// <seealso cref="IActionReceiver.OnActionReceived"/>
void WriteDiscreteActionMask(IDiscreteActionMask actionMask);

2
com.unity.ml-agents/Runtime/Actuators/IDiscreteActionMask.cs


///
/// See [Agents - Actions] for more information on masking actions.
///
/// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_8_docs/docs/Learning-Environment-Design-Agents.md#actions
/// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_9_docs/docs/Learning-Environment-Design-Agents.md#actions
/// </remarks>
/// <param name="branch">The branch for which the actions will be masked.</param>
/// <param name="actionIndices">The indices of the masked actions.</param>

26
com.unity.ml-agents/Runtime/Agent.cs


/// [OnDisable()]: https://docs.unity3d.com/ScriptReference/MonoBehaviour.OnDisable.html]
/// [OnBeforeSerialize()]: https://docs.unity3d.com/ScriptReference/MonoBehaviour.OnBeforeSerialize.html
/// [OnAfterSerialize()]: https://docs.unity3d.com/ScriptReference/MonoBehaviour.OnAfterSerialize.html
/// [Agents]: https://github.com/Unity-Technologies/ml-agents/blob/release_8_docs/docs/Learning-Environment-Design-Agents.md
/// [Reinforcement Learning in Unity]: https://github.com/Unity-Technologies/ml-agents/blob/release_8_docs/docs/Learning-Environment-Design.md
/// [Agents]: https://github.com/Unity-Technologies/ml-agents/blob/release_9_docs/docs/Learning-Environment-Design-Agents.md
/// [Reinforcement Learning in Unity]: https://github.com/Unity-Technologies/ml-agents/blob/release_9_docs/docs/Learning-Environment-Design.md
/// [Unity ML-Agents Toolkit manual]: https://github.com/Unity-Technologies/ml-agents/blob/release_8_docs/docs/Readme.md
/// [Unity ML-Agents Toolkit manual]: https://github.com/Unity-Technologies/ml-agents/blob/release_9_docs/docs/Readme.md
[HelpURL("https://github.com/Unity-Technologies/ml-agents/blob/release_8_docs/" +
[HelpURL("https://github.com/Unity-Technologies/ml-agents/blob/release_9_docs/" +
"docs/Learning-Environment-Design-Agents.md")]
[Serializable]
[RequireComponent(typeof(BehaviorParameters))]

/// for information about mixing reward signals from curiosity and Generative Adversarial
/// Imitation Learning (GAIL) with rewards supplied through this method.
///
/// [Agents - Rewards]: https://github.com/Unity-Technologies/ml-agents/blob/release_8_docs/docs/Learning-Environment-Design-Agents.md#rewards
/// [Reward Signals]: https://github.com/Unity-Technologies/ml-agents/blob/release_8_docs/docs/ML-Agents-Overview.md#a-quick-note-on-reward-signals
/// [Agents - Rewards]: https://github.com/Unity-Technologies/ml-agents/blob/release_9_docs/docs/Learning-Environment-Design-Agents.md#rewards
/// [Reward Signals]: https://github.com/Unity-Technologies/ml-agents/blob/release_9_docs/docs/ML-Agents-Overview.md#a-quick-note-on-reward-signals
/// </remarks>
/// <param name="reward">The new value of the reward.</param>
public void SetReward(float reward)

/// for information about mixing reward signals from curiosity and Generative Adversarial
/// Imitation Learning (GAIL) with rewards supplied through this method.
///
/// [Agents - Rewards]: https://github.com/Unity-Technologies/ml-agents/blob/release_8_docs/docs/Learning-Environment-Design-Agents.md#rewards
/// [Reward Signals]: https://github.com/Unity-Technologies/ml-agents/blob/release_8_docs/docs/ML-Agents-Overview.md#a-quick-note-on-reward-signals
/// [Agents - Rewards]: https://github.com/Unity-Technologies/ml-agents/blob/release_9_docs/docs/Learning-Environment-Design-Agents.md#rewards
/// [Reward Signals]: https://github.com/Unity-Technologies/ml-agents/blob/release_9_docs/docs/ML-Agents-Overview.md#a-quick-note-on-reward-signals
///</remarks>
/// <param name="increment">Incremental reward value.</param>
public void AddReward(float increment)

/// implementing a simple heuristic function can aid in debugging agent actions and interactions
/// with its environment.
///
/// [Demonstration Recorder]: https://github.com/Unity-Technologies/ml-agents/blob/release_8_docs/docs/Learning-Environment-Design-Agents.md#recording-demonstrations
/// [Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_8_docs/docs/Learning-Environment-Design-Agents.md#actions
/// [Demonstration Recorder]: https://github.com/Unity-Technologies/ml-agents/blob/release_9_docs/docs/Learning-Environment-Design-Agents.md#recording-demonstrations
/// [Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_9_docs/docs/Learning-Environment-Design-Agents.md#actions
/// [GameObject]: https://docs.unity3d.com/Manual/GameObjects.html
/// </remarks>
/// <example>

/// For more information about observations, see [Observations and Sensors].
///
/// [GameObject]: https://docs.unity3d.com/Manual/GameObjects.html
/// [Observations and Sensors]: https://github.com/Unity-Technologies/ml-agents/blob/release_8_docs/docs/Learning-Environment-Design-Agents.md#observations-and-sensors
/// [Observations and Sensors]: https://github.com/Unity-Technologies/ml-agents/blob/release_9_docs/docs/Learning-Environment-Design-Agents.md#observations-and-sensors
/// </remarks>
public virtual void CollectObservations(VectorSensor sensor)
{

///
/// See [Agents - Actions] for more information on masking actions.
///
/// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_8_docs/docs/Learning-Environment-Design-Agents.md#actions
/// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_9_docs/docs/Learning-Environment-Design-Agents.md#actions
/// </remarks>
/// <seealso cref="IActionReceiver.OnActionReceived"/>
public virtual void WriteDiscreteActionMask(IDiscreteActionMask actionMask)

///
/// For more information about implementing agent actions see [Agents - Actions].
///
/// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_8_docs/docs/Learning-Environment-Design-Agents.md#actions
/// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_9_docs/docs/Learning-Environment-Design-Agents.md#actions
/// </remarks>
/// <param name="actions">
/// Struct containing the buffers of actions to be executed at this step.

2
com.unity.ml-agents/Runtime/Demonstrations/DemonstrationRecorder.cs


/// See [Imitation Learning - Recording Demonstrations] for more information.
///
/// [GameObject]: https://docs.unity3d.com/Manual/GameObjects.html
/// [Imitation Learning - Recording Demonstrations]: https://github.com/Unity-Technologies/ml-agents/blob/release_8_docs/docs//Learning-Environment-Design-Agents.md#recording-demonstrations
/// [Imitation Learning - Recording Demonstrations]: https://github.com/Unity-Technologies/ml-agents/blob/release_9_docs/docs//Learning-Environment-Design-Agents.md#recording-demonstrations
/// </remarks>
[RequireComponent(typeof(Agent))]
[AddComponentMenu("ML Agents/Demonstration Recorder", (int)MenuGroup.Default)]

2
com.unity.ml-agents/Runtime/DiscreteActionMasker.cs


///
/// See [Agents - Actions] for more information on masking actions.
///
/// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_8_docs/docs/Learning-Environment-Design-Agents.md#actions
/// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/release_9_docs/docs/Learning-Environment-Design-Agents.md#actions
/// </remarks>
/// <param name="branch">The branch for which the actions will be masked.</param>
/// <param name="actionIndices">The indices of the masked actions.</param>

2
com.unity.ml-agents/Runtime/SensorHelper.cs


if (expected[h, w, c] != output[tensorShape.Index(0, h, w, c)])
{
errorMessage = $"Expected and actual differed in position [{h}, {w}, {c}]. " +
"Expected: {expected[h, w, c]} Actual: {output[tensorShape.Index(0, h, w, c)]} ";
$"Expected: {expected[h, w, c]} Actual: {output[tensorShape.Index(0, h, w, c)]} ";
return false;
}
}

4
docs/Installation-Anaconda-Windows.md


the ml-agents Conda environment by typing `activate ml-agents`)_:
```sh
git clone --branch release_8 https://github.com/Unity-Technologies/ml-agents.git
git clone --branch release_9 https://github.com/Unity-Technologies/ml-agents.git
The `--branch release_8` option will switch to the tag of the latest stable
The `--branch release_9` option will switch to the tag of the latest stable
release. Omitting that will get the `master` branch which is potentially
unstable.

6
docs/Installation.md


of our tutorials / guides assume you have access to our example environments).
```sh
git clone --branch release_8 https://github.com/Unity-Technologies/ml-agents.git
git clone --branch release_9 https://github.com/Unity-Technologies/ml-agents.git
The `--branch release_8` option will switch to the tag of the latest stable
The `--branch release_9` option will switch to the tag of the latest stable
release. Omitting that will get the `master` branch which is potentially
unstable.

ML-Agents Toolkit for your purposes. If you plan to contribute those changes
back, make sure to clone the `master` branch (by omitting `--branch release_8`
back, make sure to clone the `master` branch (by omitting `--branch release_9`
from the command above). See our
[Contributions Guidelines](../com.unity.ml-agents/CONTRIBUTING.md) for more
information on contributing to the ML-Agents Toolkit.

21
docs/Learning-Environment-Examples.md


does not train with the provided default training parameters.**
- Float Properties: None
- Benchmark Mean Reward: 1.75
## Match 3
![Match 3](images/match3.png)
- Set-up: Simple match-3 game. Matched pieces are removed, and remaining pieces
drop down. New pieces are spawned randomly at the top, with a chance of being
"special".
- Goal: Maximize score from matching pieces.
- Agents: The environment contains several independent Agents.
- Agent Reward Function (independent):
- .01 for each normal piece cleared. Special pieces are worth 2x or 3x.
- Behavior Parameters:
- None
- Observations and actions are defined with a sensor and actuator respectively.
- Float Properties: None
- Benchmark Mean Reward:
- 37.2 for visual observations
- 37.6 for vector observations
- 34.2 for simple heuristic (pick a random valid move)
- 37.0 for greedy heuristic (pick the highest-scoring valid move)

2
docs/Training-on-Amazon-Web-Service.md


2. Clone the ML-Agents repo and install the required Python packages
```sh
git clone --branch release_8 https://github.com/Unity-Technologies/ml-agents.git
git clone --branch release_9 https://github.com/Unity-Technologies/ml-agents.git
cd ml-agents/ml-agents/
pip3 install -e .
```

4
docs/Unity-Inference-Engine.md


loading expects certain conventions for constants and tensor names. While it is
possible to construct a model that follows these conventions, we don't provide
any additional help for this. More details can be found in
[TensorNames.cs](https://github.com/Unity-Technologies/ml-agents/blob/release_8_docs/com.unity.ml-agents/Runtime/Inference/TensorNames.cs)
[TensorNames.cs](https://github.com/Unity-Technologies/ml-agents/blob/release_9_docs/com.unity.ml-agents/Runtime/Inference/TensorNames.cs)
[BarracudaModelParamLoader.cs](https://github.com/Unity-Technologies/ml-agents/blob/release_8_docs/com.unity.ml-agents/Runtime/Inference/BarracudaModelParamLoader.cs).
[BarracudaModelParamLoader.cs](https://github.com/Unity-Technologies/ml-agents/blob/release_9_docs/com.unity.ml-agents/Runtime/Inference/BarracudaModelParamLoader.cs).
If you wish to run inference on an externally trained model, you should use
Barracuda directly, instead of trying to run it through ML-Agents.

1
utils/make_readme_table.py


ReleaseInfo("release_6", "1.3.0", "0.19.0", "August 12, 2020"),
ReleaseInfo("release_7", "1.4.0", "0.20.0", "September 16, 2020"),
ReleaseInfo("release_8", "1.5.0", "0.21.0", "October 14, 2020"),
ReleaseInfo("release_9", "1.5.0", "0.21.1", "November 4, 2020"),
]
MAX_DAYS = 150 # do not print releases older than this many days

8
Project/Assets/ML-Agents/Examples/Match3.meta


fileFormatVersion: 2
guid: 85094c6352d9e43c497a54fef35e4d76
folderAsset: yes
DefaultImporter:
externalObjects: {}
userData:
assetBundleName:
assetBundleVariant:

67
com.unity.ml-agents.extensions/Documentation~/Match3.md


# Match-3 Game Support
We provide some utilities to integrate ML-Agents with Match-3 games.
## 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
* ask your game whether a move is allowed
* request that your game make a move
These are handled by implementing the `GetCellType()`, `IsMoveValid()`, and `MakeMove()` abstract methods.
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)`
Returns the "color" of 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.
#### `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 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(NumRows, NumColumns)`. There are two helper
functions to create a new `Move`:
* `public static Move FromMoveIndex(int moveIndex, int maxRows, int maxCols)` 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, int maxRows, int maxCols)` creates
a `Move` from a row, column, and direction (and board size).
## `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 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.
## `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.

3
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behaviors:
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trainer_type: ppo
hyperparameters:
batch_size: 64
buffer_size: 12000
learning_rate: 0.0003
beta: 0.001
epsilon: 0.2
lambd: 0.99
num_epoch: 3
learning_rate_schedule: constant
network_settings:
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hidden_units: 128
num_layers: 2
vis_encode_type: match3
reward_signals:
extrinsic:
gamma: 0.99
strength: 1.0
keep_checkpoints: 5
max_steps: 5000000
time_horizon: 1000
summary_freq: 10000
threaded: true
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hyperparameters:
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buffer_size: 12000
learning_rate: 0.0003
beta: 0.001
epsilon: 0.2
lambd: 0.99
num_epoch: 3
learning_rate_schedule: constant
network_settings:
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hidden_units: 128
num_layers: 2
vis_encode_type: match3
reward_signals:
extrinsic:
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strength: 1.0
keep_checkpoints: 5
max_steps: 5000000
time_horizon: 1000
summary_freq: 10000
threaded: true
Match3SimpleHeuristic:
# Settings can be very simple since we don't care about actually training the model
trainer_type: ppo
hyperparameters:
batch_size: 64
buffer_size: 128
network_settings:
hidden_units: 4
num_layers: 1
max_steps: 5000000
summary_freq: 10000
threaded: true
Match3GreedyHeuristic:
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trainer_type: ppo
hyperparameters:
batch_size: 64
buffer_size: 128
network_settings:
hidden_units: 4
num_layers: 1
max_steps: 5000000
summary_freq: 10000
threaded: true

77
docs/images/match3.png

之前 之后
宽度: 297  |  高度: 320  |  大小: 22 KiB

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Project/Assets/ML-Agents/Examples/Match3/Scenes/Match3.unity
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373
Project/Assets/ML-Agents/Examples/Match3/Scripts/Match3Agent.cs


using System;
using UnityEngine;
using Unity.MLAgents;
using Unity.MLAgents.Actuators;
using Unity.MLAgents.Extensions.Match3;
namespace Unity.MLAgentsExamples
{
/// <summary>
/// State of the "game" when showing all steps of the simulation. This is only used outside of training.
/// The state diagram is
///
/// | <--------------------------------------- ^
/// | |
/// v |
/// +--------+ +-------+ +-----+ +------+
/// |Find | ---> |Clear | ---> |Drop | ---> |Fill |
/// |Matches | |Matched| | | |Empty |
/// +--------+ +-------+ +-----+ +------+
///
/// | ^
/// | |
/// v |
///
/// +--------+
/// |Wait for|
/// |Move |
/// +--------+
///
/// The stats advances each "MoveTime" seconds.
/// </summary>
enum State
{
/// <summary>
/// Guard value, should never happen.
/// </summary>
Invalid = -1,
/// <summary>
/// Look for matches. If there are matches, the next state is ClearMatched, otherwise WaitForMove.
/// </summary>
FindMatches = 0,
/// <summary>
/// Remove matched cells and replace them with a placeholder value.
/// </summary>
ClearMatched = 1,
/// <summary>
/// Move cells "down" to fill empty space.
/// </summary>
Drop = 2,
/// <summary>
/// Replace empty cells with new random values.
/// </summary>
FillEmpty = 3,
/// <summary>
/// Request a move from the Agent.
/// </summary>
WaitForMove = 4,
}
public enum HeuristicQuality
{
/// <summary>
/// The heuristic will pick any valid move at random.
/// </summary>
RandomValidMove,
/// <summary>
/// The heuristic will pick the move that scores the most points.
/// This only looks at the immediate move, and doesn't consider where cells will fall.
/// </summary>
Greedy
}
public class Match3Agent : Agent
{
[HideInInspector]
public Match3Board Board;
public float MoveTime = 1.0f;
public int MaxMoves = 500;
public HeuristicQuality HeuristicQuality = HeuristicQuality.RandomValidMove;
State m_CurrentState = State.WaitForMove;
float m_TimeUntilMove;
private int m_MovesMade;
private System.Random m_Random;
private const float k_RewardMultiplier = 0.01f;
void Awake()
{
Board = GetComponent<Match3Board>();
var seed = Board.RandomSeed == -1 ? gameObject.GetInstanceID() : Board.RandomSeed + 1;
m_Random = new System.Random(seed);
}
public override void OnEpisodeBegin()
{
base.OnEpisodeBegin();
Board.InitSettled();
m_CurrentState = State.FindMatches;
m_TimeUntilMove = MoveTime;
m_MovesMade = 0;
}
private void FixedUpdate()
{
if (Academy.Instance.IsCommunicatorOn)
{
FastUpdate();
}
else
{
AnimatedUpdate();
}
// We can't use the normal MaxSteps system to decide when to end an episode,
// since different agents will make moves at different frequencies (depending on the number of
// chained moves). So track a number of moves per Agent and manually interrupt the episode.
if (m_MovesMade >= MaxMoves)
{
EpisodeInterrupted();
}
}
void FastUpdate()
{
while (true)
{
var hasMatched = Board.MarkMatchedCells();
if (!hasMatched)
{
break;
}
var pointsEarned = Board.ClearMatchedCells();
AddReward(k_RewardMultiplier * pointsEarned);
Board.DropCells();
Board.FillFromAbove();
}
while (!HasValidMoves())
{
// Shuffle the board until we have a valid move.
Board.InitSettled();
}
RequestDecision();
m_MovesMade++;
}
void AnimatedUpdate()
{
m_TimeUntilMove -= Time.deltaTime;
if (m_TimeUntilMove > 0.0f)
{
return;
}
m_TimeUntilMove = MoveTime;
var nextState = State.Invalid;
switch (m_CurrentState)
{
case State.FindMatches:
var hasMatched = Board.MarkMatchedCells();
nextState = hasMatched ? State.ClearMatched : State.WaitForMove;
if (nextState == State.WaitForMove)
{
m_MovesMade++;
}
break;
case State.ClearMatched:
var pointsEarned = Board.ClearMatchedCells();
AddReward(k_RewardMultiplier * pointsEarned);
nextState = State.Drop;
break;
case State.Drop:
Board.DropCells();
nextState = State.FillEmpty;
break;
case State.FillEmpty:
Board.FillFromAbove();
nextState = State.FindMatches;
break;
case State.WaitForMove:
while (true)
{
// Shuffle the board until we have a valid move.
bool hasMoves = HasValidMoves();
if (hasMoves)
{
break;
}
Board.InitSettled();
}
RequestDecision();
nextState = State.FindMatches;
break;
default:
throw new ArgumentOutOfRangeException();
}
m_CurrentState = nextState;
}
bool HasValidMoves()
{
foreach (var move in Board.ValidMoves())
{
return true;
}
return false;
}
public override void Heuristic(in ActionBuffers actionsOut)
{
var discreteActions = actionsOut.DiscreteActions;
discreteActions[0] = GreedyMove();
}
int GreedyMove()
{
var pointsByType = new[] { Board.BasicCellPoints, Board.SpecialCell1Points, Board.SpecialCell2Points };
var bestMoveIndex = 0;
var bestMovePoints = -1;
var numMovesAtCurrentScore = 0;
foreach (var move in Board.ValidMoves())
{
var movePoints = HeuristicQuality == HeuristicQuality.Greedy ? EvalMovePoints(move, pointsByType) : 1;
if (movePoints < bestMovePoints)
{
// Worse, skip
continue;
}
if (movePoints > bestMovePoints)
{
// Better, keep
bestMovePoints = movePoints;
bestMoveIndex = move.MoveIndex;
numMovesAtCurrentScore = 1;
}
else
{
// Tied for best - use reservoir sampling to make sure we select from equal moves uniformly.
// See https://en.wikipedia.org/wiki/Reservoir_sampling#Simple_algorithm
numMovesAtCurrentScore++;
var randVal = m_Random.Next(0, numMovesAtCurrentScore);
if (randVal == 0)
{
// Keep the new one
bestMoveIndex = move.MoveIndex;
}
}
}
return bestMoveIndex;
}
int EvalMovePoints(Move move, int[] pointsByType)
{
// Counts the expected points for making the move.
var moveVal = Board.GetCellType(move.Row, move.Column);
var moveSpecial = Board.GetSpecialType(move.Row, move.Column);
var (otherRow, otherCol) = move.OtherCell();
var oppositeVal = Board.GetCellType(otherRow, otherCol);
var oppositeSpecial = Board.GetSpecialType(otherRow, otherCol);
int movePoints = EvalHalfMove(
otherRow, otherCol, moveVal, moveSpecial, move.Direction, pointsByType
);
int otherPoints = EvalHalfMove(
move.Row, move.Column, oppositeVal, oppositeSpecial, move.OtherDirection(), pointsByType
);
return movePoints + otherPoints;
}
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.
int matchedLeft = 0, matchedRight = 0, matchedUp = 0, matchedDown = 0;
int scoreLeft = 0, scoreRight = 0, scoreUp = 0, scoreDown = 0;
if (incomingDirection != Direction.Right)
{
for (var c = newCol - 1; c >= 0; c--)
{
if (Board.GetCellType(newRow, c) == newValue)
{
matchedLeft++;
scoreLeft += pointsByType[Board.GetSpecialType(newRow, c)];
}
else
break;
}
}
if (incomingDirection != Direction.Left)
{
for (var c = newCol + 1; c < Board.Columns; c++)
{
if (Board.GetCellType(newRow, c) == newValue)
{
matchedRight++;
scoreRight += pointsByType[Board.GetSpecialType(newRow, c)];
}
else
break;
}
}
if (incomingDirection != Direction.Down)
{
for (var r = newRow + 1; r < Board.Rows; r++)
{
if (Board.GetCellType(r, newCol) == newValue)
{
matchedUp++;
scoreUp += pointsByType[Board.GetSpecialType(r, newCol)];
}
else
break;
}
}
if (incomingDirection != Direction.Up)
{
for (var r = newRow - 1; r >= 0; r--)
{
if (Board.GetCellType(r, newCol) == newValue)
{
matchedDown++;
scoreDown += pointsByType[Board.GetSpecialType(r, newCol)];
}
else
break;
}
}
if ((matchedUp + matchedDown >= 2) || (matchedLeft + matchedRight >= 2))
{
// It's a match. Start from counting the piece being moved
var totalScore = pointsByType[newSpecial];
if (matchedUp + matchedDown >= 2)
{
totalScore += scoreUp + scoreDown;
}
if (matchedLeft + matchedRight >= 2)
{
totalScore += scoreLeft + scoreRight;
}
return totalScore;
}
return 0;
}
}
}

3
Project/Assets/ML-Agents/Examples/Match3/Scripts/Match3Agent.cs.meta


fileFormatVersion: 2
guid: d982f0cd92214bd2b689be838fa40c44
timeCreated: 1598221207

272
Project/Assets/ML-Agents/Examples/Match3/Scripts/Match3Board.cs


using Unity.MLAgents.Extensions.Match3;
using UnityEngine;
namespace Unity.MLAgentsExamples
{
public class Match3Board : AbstractBoard
{
public int RandomSeed = -1;
public const int k_EmptyCell = -1;
[Tooltip("Points earned for clearing a basic cell (cube)")]
public int BasicCellPoints = 1;
[Tooltip("Points earned for clearing a special cell (sphere)")]
public int SpecialCell1Points = 2;
[Tooltip("Points earned for clearing an extra special cell (plus)")]
public int SpecialCell2Points = 3;
(int, int)[,] m_Cells;
bool[,] m_Matched;
System.Random m_Random;
void Awake()
{
m_Cells = new (int, int)[Columns, Rows];
m_Matched = new bool[Columns, Rows];
m_Random = new System.Random(RandomSeed == -1 ? gameObject.GetInstanceID() : RandomSeed);
InitRandom();
}
public override bool MakeMove(Move move)
{
if (!IsMoveValid(move))
{
return false;
}
var originalValue = m_Cells[move.Column, move.Row];
var (otherRow, otherCol) = move.OtherCell();
var destinationValue = m_Cells[otherCol, otherRow];
m_Cells[move.Column, move.Row] = destinationValue;
m_Cells[otherCol, otherRow] = originalValue;
return true;
}
public override int GetCellType(int row, int col)
{
return m_Cells[col, row].Item1;
}
public override int GetSpecialType(int row, int col)
{
return m_Cells[col, row].Item2;
}
public override bool IsMoveValid(Move m)
{
if (m_Cells == null)
{
return false;
}
return SimpleIsMoveValid(m);
}
public bool MarkMatchedCells(int[,] cells = null)
{
ClearMarked();
bool madeMatch = false;
for (var i = 0; i < Rows; i++)
{
for (var j = 0; j < Columns; j++)
{
// Check vertically
var matchedRows = 0;
for (var iOffset = i; iOffset < Rows; iOffset++)
{
if (m_Cells[j, i].Item1 != m_Cells[j, iOffset].Item1)
{
break;
}
matchedRows++;
}
if (matchedRows >= 3)
{
madeMatch = true;
for (var k = 0; k < matchedRows; k++)
{
m_Matched[j, i + k] = true;
}
}
// Check vertically
var matchedCols = 0;
for (var jOffset = j; jOffset < Columns; jOffset++)
{
if (m_Cells[j, i].Item1 != m_Cells[jOffset, i].Item1)
{
break;
}
matchedCols++;
}
if (matchedCols >= 3)
{
madeMatch = true;
for (var k = 0; k < matchedCols; k++)
{
m_Matched[j + k, i] = true;
}
}
}
}
return madeMatch;
}
/// <summary>
/// Sets cells that are matched to the empty cell, and returns the score earned.
/// </summary>
/// <returns></returns>
public int ClearMatchedCells()
{
var pointsByType = new[] { BasicCellPoints, SpecialCell1Points, SpecialCell2Points };
int pointsEarned = 0;
for (var i = 0; i < Rows; i++)
{
for (var j = 0; j < Columns; j++)
{
if (m_Matched[j, i])
{
var speciaType = GetSpecialType(i, j);
pointsEarned += pointsByType[speciaType];
m_Cells[j, i] = (k_EmptyCell, 0);
}
}
}
ClearMarked(); // TODO clear here or at start of matching?
return pointsEarned;
}
public bool DropCells()
{
var madeChanges = false;
// Gravity is applied in the negative row direction
for (var j = 0; j < Columns; j++)
{
var writeIndex = 0;
for (var readIndex = 0; readIndex < Rows; readIndex++)
{
m_Cells[j, writeIndex] = m_Cells[j, readIndex];
if (m_Cells[j, readIndex].Item1 != k_EmptyCell)
{
writeIndex++;
}
}
// Fill in empties at the end
for (; writeIndex < Rows; writeIndex++)
{
madeChanges = true;
m_Cells[j, writeIndex] = (k_EmptyCell, 0);
}
}
return madeChanges;
}
public bool FillFromAbove()
{
bool madeChanges = false;
for (var i = 0; i < Rows; i++)
{
for (var j = 0; j < Columns; j++)
{
if (m_Cells[j, i].Item1 == k_EmptyCell)
{
madeChanges = true;
m_Cells[j, i] = (GetRandomCellType(), GetRandomSpecialType());
}
}
}
return madeChanges;
}
public (int, int)[,] Cells
{
get { return m_Cells; }
}
public bool[,] Matched
{
get { return m_Matched; }
}
// Initialize the board to random values.
public void InitRandom()
{
for (var i = 0; i < Rows; i++)
{
for (var j = 0; j < Columns; j++)
{
m_Cells[j, i] = (GetRandomCellType(), GetRandomSpecialType());
}
}
}
public void InitSettled()
{
InitRandom();
while (true)
{
var anyMatched = MarkMatchedCells();
if (!anyMatched)
{
return;
}
ClearMatchedCells();
DropCells();
FillFromAbove();
}
}
void ClearMarked()
{
for (var i = 0; i < Rows; i++)
{
for (var j = 0; j < Columns; j++)
{
m_Matched[j, i] = false;
}
}
}
int GetRandomCellType()
{
return m_Random.Next(0, NumCellTypes);
}
int GetRandomSpecialType()
{
// 1 in N chance to get a type-2 special
// 2 in N chance to get a type-1 special
// otherwise 0 (boring)
var N = 10;
var val = m_Random.Next(0, N);
if (val == 0)
{
return 2;
}
if (val <= 2)
{
return 1;
}
return 0;
}
}
}

11
Project/Assets/ML-Agents/Examples/Match3/Scripts/Match3Board.cs.meta


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102
Project/Assets/ML-Agents/Examples/Match3/Scripts/Match3Drawer.cs


using UnityEngine;
using Unity.MLAgents.Extensions.Match3;
namespace Unity.MLAgentsExamples
{
public class Match3Drawer : MonoBehaviour
{
public int DebugMoveIndex = -1;
static Color[] s_Colors = new[]
{
Color.red,
Color.green,
Color.blue,
Color.cyan,
Color.magenta,
Color.yellow,
Color.gray,
Color.black,
};
private static Color s_EmptyColor = new Color(0.5f, 0.5f, 0.5f, .25f);
void OnDrawGizmos()
{
// TODO replace Gizmos for drawing the game state with proper GameObjects and animations.
var cubeSize = .5f;
var cubeSpacing = .75f;
var matchedWireframeSize = .5f * (cubeSize + cubeSpacing);
var board = GetComponent<Match3Board>();
if (board == null)
{
return;
}
for (var i = 0; i < board.Rows; i++)
{
for (var j = 0; j < board.Columns; j++)
{
var value = board.Cells != null ? board.GetCellType(i, j) : Match3Board.k_EmptyCell;
if (value >= 0 && value < s_Colors.Length)
{
Gizmos.color = s_Colors[value];
}
else
{
Gizmos.color = s_EmptyColor;
}
var pos = new Vector3(j, i, 0);
pos *= cubeSpacing;
var specialType = board.Cells != null ? board.GetSpecialType(i, j) : 0;
if (specialType == 2)
{
Gizmos.DrawCube(transform.TransformPoint(pos), cubeSize * new Vector3(1f, .5f, .5f));
Gizmos.DrawCube(transform.TransformPoint(pos), cubeSize * new Vector3(.5f, 1f, .5f));
Gizmos.DrawCube(transform.TransformPoint(pos), cubeSize * new Vector3(.5f, .5f, 1f));
}
else if (specialType == 1)
{
Gizmos.DrawSphere(transform.TransformPoint(pos), .5f * cubeSize);
}
else
{
Gizmos.DrawCube(transform.TransformPoint(pos), cubeSize * Vector3.one);
}
Gizmos.color = Color.yellow;
if (board.Matched != null && board.Matched[j, i])
{
Gizmos.DrawWireCube(transform.TransformPoint(pos), matchedWireframeSize * Vector3.one);
}
}
}
// Draw valid moves
foreach (var move in board.AllMoves())
{
if (DebugMoveIndex >= 0 && move.MoveIndex != DebugMoveIndex)
{
continue;
}
if (!board.IsMoveValid(move))
{
continue;
}
var (otherRow, otherCol) = move.OtherCell();
var pos = new Vector3(move.Column, move.Row, 0) * cubeSpacing;
var otherPos = new Vector3(otherCol, otherRow, 0) * cubeSpacing;
var oneQuarter = Vector3.Lerp(pos, otherPos, .25f);
var threeQuarters = Vector3.Lerp(pos, otherPos, .75f);
Gizmos.DrawLine(transform.TransformPoint(oneQuarter), transform.TransformPoint(threeQuarters));
}
}
}
}

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233
com.unity.ml-agents.extensions/Runtime/Match3/AbstractBoard.cs


using System;
using System.Collections.Generic;
using UnityEngine;
namespace Unity.MLAgents.Extensions.Match3
{
public abstract class AbstractBoard : MonoBehaviour
{
/// <summary>
/// Number of rows on the board
/// </summary>
public int Rows;
/// <summary>
/// Number of columns on the board
/// </summary>
public int Columns;
/// <summary>
/// Maximum number of different types of cells (colors, pieces, etc).
/// </summary>
public int NumCellTypes;
/// <summary>
/// Maximum number of special types. This can be zero, in which case
/// all cells of the same type are assumed to be equivalent.
/// </summary>
public int NumSpecialTypes;
/// <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.
/// </summary>
/// <param name="row"></param>
/// <param name="col"></param>
/// <returns></returns>
public abstract int GetCellType(int row, int col);
/// <summary>
/// Returns the special type of the piece at the given row and column.
/// This should be between 0 and NumSpecialTypes (inclusive).
/// The actual order of the values doesn't matter.
/// </summary>
/// <param name="row"></param>
/// <param name="col"></param>
/// <returns></returns>
public abstract int GetSpecialType(int row, int col);
/// <summary>
/// Check whether the particular Move is valid for the game.
/// 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>
/// <param name="m"></param>
/// <returns></returns>
public abstract bool IsMoveValid(Move m);
/// <summary>
/// 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.
/// </summary>
/// <param name="m"></param>
/// <returns></returns>
public abstract bool MakeMove(Move m);
/// <summary>
/// Return the total number of moves possible for the board.
/// </summary>
/// <returns></returns>
public int NumMoves()
{
return Move.NumPotentialMoves(Rows, Columns);
}
/// <summary>
/// An optional callback for when the all moves are invalid. Ideally, the game state should
/// be changed before this happens, but this is a way to get notified if not.
/// </summary>
public Action OnNoValidMovesAction;
/// <summary>
/// Iterate through all Moves on the board.
/// </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);
}
}
/// <summary>
/// Iterate through all valid Moves on the board.
/// </summary>
/// <returns></returns>
public IEnumerable<Move> ValidMoves()
{
var currentMove = Move.FromMoveIndex(0, Rows, Columns);
for (var i = 0; i < NumMoves(); i++)
{
if (IsMoveValid(currentMove))
{
yield return currentMove;
}
currentMove.Next(Rows, Columns);
}
}
/// <summary>
/// Iterate through all invalid Moves on the board.
/// </summary>
/// <returns></returns>
public IEnumerable<Move> InvalidMoves()
{
var currentMove = Move.FromMoveIndex(0, Rows, Columns);
for (var i = 0; i < NumMoves(); i++)
{
if (!IsMoveValid(currentMove))
{
yield return currentMove;
}
currentMove.Next(Rows, Columns);
}
}
/// <summary>
/// Returns true if swapped the cells specified by the move would result in
/// 3 or more cells of the same type in a row. This assumes that all pieces are allowed
/// to be moved; to add extra logic, incorporate it into you IsMoveValid() method.
/// </summary>
/// <param name="move"></param>
/// <returns></returns>
public bool SimpleIsMoveValid(Move move)
{
using (TimerStack.Instance.Scoped("SimpleIsMoveValid"))
{
var moveVal = GetCellType(move.Row, move.Column);
var (otherRow, otherCol) = move.OtherCell();
var oppositeVal = GetCellType(otherRow, otherCol);
// Simple check - if the values are the same, don't match
// This might not be valid for all games
{
if (moveVal == oppositeVal)
{
return false;
}
}
bool moveMatches = CheckHalfMove(otherRow, otherCol, moveVal, move.Direction);
if (moveMatches)
{
// early out
return true;
}
bool otherMatches = CheckHalfMove(move.Row, move.Column, oppositeVal, move.OtherDirection());
return otherMatches;
}
}
/// <summary>
/// Check if one of the cells that is swapped during a move matches 3 or more.
/// Since these checks are similar for each cell, we consider the Move as two "half moves".
/// </summary>
/// <param name="newRow"></param>
/// <param name="newCol"></param>
/// <param name="newValue"></param>
/// <param name="incomingDirection"></param>
/// <returns></returns>
bool CheckHalfMove(int newRow, int newCol, int newValue, Direction incomingDirection)
{
int matchedLeft = 0, matchedRight = 0, matchedUp = 0, matchedDown = 0;
if (incomingDirection != Direction.Right)
{
for (var c = newCol - 1; c >= 0; c--)
{
if (GetCellType(newRow, c) == newValue)
matchedLeft++;
else
break;
}
}
if (incomingDirection != Direction.Left)
{
for (var c = newCol + 1; c < Columns; c++)
{
if (GetCellType(newRow, c) == newValue)
matchedRight++;
else
break;
}
}
if (incomingDirection != Direction.Down)
{
for (var r = newRow + 1; r < Rows; r++)
{
if (GetCellType(r, newCol) == newValue)
matchedUp++;
else
break;
}
}
if (incomingDirection != Direction.Up)
{
for (var r = newRow - 1; r >= 0; r--)
{
if (GetCellType(r, newCol) == newValue)
matchedDown++;
else
break;
}
}
if ((matchedUp + matchedDown >= 2) || (matchedLeft + matchedRight >= 2))
{
return true;
}
return false;
}
}
}

3
com.unity.ml-agents.extensions/Runtime/Match3/AbstractBoard.cs.meta


fileFormatVersion: 2
guid: 6222defa70dc4c08aaeafd0be4e821d2
timeCreated: 1600466051

120
com.unity.ml-agents.extensions/Runtime/Match3/Match3Actuator.cs


using System.Collections.Generic;
using Unity.MLAgents.Actuators;
using UnityEngine;
namespace Unity.MLAgents.Extensions.Match3
{
/// <summary>
/// Actuator for a Match3 game. It translates valid moves (defined by AbstractBoard.IsMoveValid())
/// in action masks, and applies the action to the board via AbstractBoard.MakeMove().
/// </summary>
public class Match3Actuator : IActuator
{
private AbstractBoard m_Board;
private ActionSpec m_ActionSpec;
private bool m_ForceHeuristic;
private System.Random m_Random;
private Agent m_Agent;
private int m_Rows;
private int m_Columns;
private int m_NumCellTypes;
/// <summary>
/// Create a Match3Actuator.
/// </summary>
/// <param name="board"></param>
/// <param name="forceHeuristic">Whether the inference action should be ignored and the Agent's Heuristic
/// should be called. This should only be used for generating comparison stats of the Heuristic.</param>
/// <param name="agent"></param>
/// <param name="name"></param>
public Match3Actuator(AbstractBoard board, bool forceHeuristic, Agent agent, string name)
{
m_Board = board;
m_Rows = board.Rows;
m_Columns = board.Columns;
m_NumCellTypes = board.NumCellTypes;
Name = name;
m_ForceHeuristic = forceHeuristic;
m_Agent = agent;
var numMoves = Move.NumPotentialMoves(m_Board.Rows, m_Board.Columns);
m_ActionSpec = ActionSpec.MakeDiscrete(numMoves);
}
/// <inheritdoc/>
public ActionSpec ActionSpec => m_ActionSpec;
/// <inheritdoc/>
public void OnActionReceived(ActionBuffers actions)
{
if (m_ForceHeuristic)
{
m_Agent.Heuristic(actions);
}
var moveIndex = actions.DiscreteActions[0];
if (m_Board.Rows != m_Rows || m_Board.Columns != m_Columns || m_Board.NumCellTypes != m_NumCellTypes)
{
Debug.LogWarning(
$"Board shape changes since actuator initialization. This may cause unexpected results. " +
$"Old shape: Rows={m_Rows} Columns={m_Columns}, NumCellTypes={m_NumCellTypes} " +
$"Current shape: Rows={m_Board.Rows} Columns={m_Board.Columns}, NumCellTypes={m_Board.NumCellTypes}"
);
}
Move move = Move.FromMoveIndex(moveIndex, m_Rows, m_Columns);
m_Board.MakeMove(move);
}
/// <inheritdoc/>
public void WriteDiscreteActionMask(IDiscreteActionMask actionMask)
{
using (TimerStack.Instance.Scoped("WriteDiscreteActionMask"))
{
actionMask.WriteMask(0, InvalidMoveIndices());
}
}
/// <inheritdoc/>
public string Name { get; }
/// <inheritdoc/>
public void ResetData()
{
}
IEnumerable<int> InvalidMoveIndices()
{
var numValidMoves = m_Board.NumMoves();
foreach (var move in m_Board.InvalidMoves())
{
numValidMoves--;
if (numValidMoves == 0)
{
// If all the moves are invalid and we mask all the actions out, this will cause an assert
// later on in IDiscreteActionMask. Instead, fire a callback to the user if they provided one,
// (or log a warning if not) and leave the last action unmasked. This isn't great, but
// an invalid move should be easier to handle than an exception..
if (m_Board.OnNoValidMovesAction != null)
{
m_Board.OnNoValidMovesAction();
}
else
{
Debug.LogWarning(
"No valid moves are available. The last action will be left unmasked, so " +
"an invalid move will be passed to AbstractBoard.MakeMove()."
);
}
// This means the last move won't be returned as an invalid index.
yield break;
}
yield return move.MoveIndex;
}
}
}
}

3
com.unity.ml-agents.extensions/Runtime/Match3/Match3Actuator.cs.meta


fileFormatVersion: 2
guid: 9083fa4c35dc499aa5a86d8e7447c7cf
timeCreated: 1600906373

49
com.unity.ml-agents.extensions/Runtime/Match3/Match3ActuatorComponent.cs


using Unity.MLAgents.Actuators;
using UnityEngine;
using UnityEngine.Serialization;
namespace Unity.MLAgents.Extensions.Match3
{
/// <summary>
/// Actuator component for a Match 3 game. Generates a Match3Actuator at runtime.
/// </summary>
public class Match3ActuatorComponent : ActuatorComponent
{
/// <summary>
/// Name of the generated Match3Actuator object.
/// Note that changing this at runtime does not affect how the Agent sorts the actuators.
/// </summary>
public string ActuatorName = "Match3 Actuator";
/// <summary>
/// Force using the Agent's Heuristic() method to decide the action. This should only be used in testing.
/// </summary>
[FormerlySerializedAs("ForceRandom")]
[Tooltip("Force using the Agent's Heuristic() method to decide the action. This should only be used in testing.")]
public bool ForceHeuristic = false;
/// <inheritdoc/>
public override IActuator CreateActuator()
{
var board = GetComponent<AbstractBoard>();
var agent = GetComponentInParent<Agent>();
return new Match3Actuator(board, ForceHeuristic, agent, ActuatorName);
}
/// <inheritdoc/>
public override ActionSpec ActionSpec
{
get
{
var board = GetComponent<AbstractBoard>();
if (board == null)
{
return ActionSpec.MakeContinuous(0);
}
var numMoves = Move.NumPotentialMoves(board.Rows, board.Columns);
return ActionSpec.MakeDiscrete(numMoves);
}
}
}
}

3
com.unity.ml-agents.extensions/Runtime/Match3/Match3ActuatorComponent.cs.meta


fileFormatVersion: 2
guid: 08e4b0da54cb4d56bfcbae22dd49ab8d
timeCreated: 1600906388

297
com.unity.ml-agents.extensions/Runtime/Match3/Match3Sensor.cs


using System.Collections.Generic;
using Unity.MLAgents.Sensors;
using UnityEngine;
namespace Unity.MLAgents.Extensions.Match3
{
/// <summary>
/// Type of observations to generate.
///
/// </summary>
public enum Match3ObservationType
{
/// <summary>
/// Generate a one-hot encoding of the cell type for each cell on the board. If there are special types,
/// these will also be one-hot encoded.
/// </summary>
Vector,
/// <summary>
/// Generate a one-hot encoding of the cell type for each cell on the board, but arranged as
/// a Rows x Columns visual observation. If there are special types, these will also be one-hot encoded.
/// </summary>
UncompressedVisual,
/// <summary>
/// Generate a one-hot encoding of the cell type for each cell on the board, but arranged as
/// a Rows x Columns visual observation. If there are special types, these will also be one-hot encoded.
/// During training, these will be sent as a concatenated series of PNG images, with 3 channels per image.
/// </summary>
CompressedVisual
}
/// <summary>
/// Sensor for Match3 games. Can generate either vector, compressed visual,
/// or uncompressed visual observations. Uses AbstractBoard.GetCellType()
/// and AbstractBoard.GetSpecialType() to determine the observation values.
/// </summary>
public class Match3Sensor : ISparseChannelSensor
{
private Match3ObservationType m_ObservationType;
private AbstractBoard m_Board;
private int[] m_Shape;
private int[] m_SparseChannelMapping;
private string m_Name;
private int m_Rows;
private int m_Columns;
private int m_NumCellTypes;
private int m_NumSpecialTypes;
private ISparseChannelSensor sparseChannelSensorImplementation;
private int SpecialTypeSize
{
get { return m_NumSpecialTypes == 0 ? 0 : m_NumSpecialTypes + 1; }
}
/// <summary>
/// Create a sensor for the board with the specified observation type.
/// </summary>
/// <param name="board"></param>
/// <param name="obsType"></param>
/// <param name="name"></param>
public Match3Sensor(AbstractBoard board, Match3ObservationType obsType, string name)
{
m_Board = board;
m_Name = name;
m_Rows = board.Rows;
m_Columns = board.Columns;
m_NumCellTypes = board.NumCellTypes;
m_NumSpecialTypes = board.NumSpecialTypes;
m_ObservationType = obsType;
m_Shape = obsType == Match3ObservationType.Vector ?
new[] { m_Rows * m_Columns * (m_NumCellTypes + SpecialTypeSize) } :
new[] { m_Rows, m_Columns, m_NumCellTypes + SpecialTypeSize };
// See comment in GetCompressedObservation()
var cellTypePaddedSize = 3 * ((m_NumCellTypes + 2) / 3);
m_SparseChannelMapping = new int[cellTypePaddedSize + SpecialTypeSize];
// If we have 4 cell types and 2 special types (3 special size), we'd have
// [0, 1, 2, 3, -1, -1, 4, 5, 6]
for (var i = 0; i < m_NumCellTypes; i++)
{
m_SparseChannelMapping[i] = i;
}
for (var i = m_NumCellTypes; i < cellTypePaddedSize; i++)
{
m_SparseChannelMapping[i] = -1;
}
for (var i = 0; i < SpecialTypeSize; i++)
{
m_SparseChannelMapping[cellTypePaddedSize + i] = i + m_NumCellTypes;
}
}
/// <inheritdoc/>
public int[] GetObservationShape()
{
return m_Shape;
}
/// <inheritdoc/>
public int Write(ObservationWriter writer)
{
if (m_Board.Rows != m_Rows || m_Board.Columns != m_Columns || m_Board.NumCellTypes != m_NumCellTypes)
{
Debug.LogWarning(
$"Board shape changes since sensor initialization. This may cause unexpected results. " +
$"Old shape: Rows={m_Rows} Columns={m_Columns}, NumCellTypes={m_NumCellTypes} " +
$"Current shape: Rows={m_Board.Rows} Columns={m_Board.Columns}, NumCellTypes={m_Board.NumCellTypes}"
);
}
if (m_ObservationType == Match3ObservationType.Vector)
{
int offset = 0;
for (var r = 0; r < m_Rows; r++)
{
for (var c = 0; c < m_Columns; c++)
{
var val = m_Board.GetCellType(r, c);
for (var i = 0; i < m_NumCellTypes; i++)
{
writer[offset] = (i == val) ? 1.0f : 0.0f;
offset++;
}
if (m_NumSpecialTypes > 0)
{
var special = m_Board.GetSpecialType(r, c);
for (var i = 0; i < SpecialTypeSize; i++)
{
writer[offset] = (i == special) ? 1.0f : 0.0f;
offset++;
}
}
}
}
return offset;
}
else
{
// TODO combine loops? Only difference is inner-most statement.
int offset = 0;
for (var r = 0; r < m_Rows; r++)
{
for (var c = 0; c < m_Columns; c++)
{
var val = m_Board.GetCellType(r, c);
for (var i = 0; i < m_NumCellTypes; i++)
{
writer[r, c, i] = (i == val) ? 1.0f : 0.0f;
offset++;
}
if (m_NumSpecialTypes > 0)
{
var special = m_Board.GetSpecialType(r, c);
for (var i = 0; i < SpecialTypeSize; i++)
{
writer[offset] = (i == special) ? 1.0f : 0.0f;
offset++;
}
}
}
}
return offset;
}
}
/// <inheritdoc/>
public byte[] GetCompressedObservation()
{
var height = m_Rows;
var width = m_Columns;
var tempTexture = new Texture2D(width, height, TextureFormat.RGB24, false);
var converter = new OneHotToTextureUtil(height, width);
var bytesOut = new List<byte>();
// Encode the cell types and special types as separate batches of PNGs
// This is potentially wasteful, e.g. if there are 4 cell types and 1 special type, we could
// fit in in 2 images, but we'll use 3 here (2 PNGs for the 4 cell type channels, and 1 for
// the special types). Note that we have to also implement the sparse channel mapping.
// Optimize this it later.
var numCellImages = (m_NumCellTypes + 2) / 3;
for (var i = 0; i < numCellImages; i++)
{
converter.EncodeToTexture(m_Board.GetCellType, tempTexture, 3 * i);
bytesOut.AddRange(tempTexture.EncodeToPNG());
}
var numSpecialImages = (SpecialTypeSize + 2) / 3;
for (var i = 0; i < numSpecialImages; i++)
{
converter.EncodeToTexture(m_Board.GetSpecialType, tempTexture, 3 * i);
bytesOut.AddRange(tempTexture.EncodeToPNG());
}
DestroyTexture(tempTexture);
return bytesOut.ToArray();
}
/// <inheritdoc/>
public void Update()
{
}
/// <inheritdoc/>
public void Reset()
{
}
/// <inheritdoc/>
public SensorCompressionType GetCompressionType()
{
return m_ObservationType == Match3ObservationType.CompressedVisual ?
SensorCompressionType.PNG :
SensorCompressionType.None;
}
/// <inheritdoc/>
public string GetName()
{
return m_Name;
}
/// <inheritdoc/>
public int[] GetCompressedChannelMapping()
{
return m_SparseChannelMapping;
}
static void DestroyTexture(Texture2D texture)
{
if (Application.isEditor)
{
// Edit Mode tests complain if we use Destroy()
Object.DestroyImmediate(texture);
}
else
{
Object.Destroy(texture);
}
}
}
/// <summary>
/// Utility class for converting a 2D array of ints representing a one-hot encoding into
/// a texture, suitable for conversion to PNGs for observations.
/// Works by encoding 3 values at a time as pixels in the texture, thus it should be
/// called (maxValue + 2) / 3 times, increasing the channelOffset by 3 each time.
/// </summary>
internal class OneHotToTextureUtil
{
Color[] m_Colors;
int m_Height;
int m_Width;
private static Color[] s_OneHotColors = { Color.red, Color.green, Color.blue };
public delegate int GridValueProvider(int x, int y);
public OneHotToTextureUtil(int height, int width)
{
m_Colors = new Color[height * width];
m_Height = height;
m_Width = width;
}
public void EncodeToTexture(GridValueProvider gridValueProvider, Texture2D texture, int channelOffset)
{
var i = 0;
// There's an implicit flip converting to PNG from texture, so make sure we
// counteract that when forming the texture by iterating through h in reverse.
for (var h = m_Height - 1; h >= 0; h--)
{
for (var w = 0; w < m_Width; w++)
{
int oneHotValue = gridValueProvider(h, w);
if (oneHotValue < channelOffset || oneHotValue >= channelOffset + 3)
{
m_Colors[i++] = Color.black;
}
else
{
m_Colors[i++] = s_OneHotColors[oneHotValue - channelOffset];
}
}
}
texture.SetPixels(m_Colors);
}
}
}

3
com.unity.ml-agents.extensions/Runtime/Match3/Match3Sensor.cs.meta


fileFormatVersion: 2
guid: 795ad5f211e344e5bf3049abd9499721
timeCreated: 1600906663

43
com.unity.ml-agents.extensions/Runtime/Match3/Match3SensorComponent.cs


using Unity.MLAgents.Sensors;
namespace Unity.MLAgents.Extensions.Match3
{
/// <summary>
/// Sensor component for a Match3 game.
/// </summary>
public class Match3SensorComponent : SensorComponent
{
/// <summary>
/// Name of the generated Match3Sensor object.
/// Note that changing this at runtime does not affect how the Agent sorts the sensors.
/// </summary>
public string SensorName = "Match3 Sensor";
/// <summary>
/// Type of observation to generate.
/// </summary>
public Match3ObservationType ObservationType = Match3ObservationType.Vector;
/// <inheritdoc/>
public override ISensor CreateSensor()
{
var board = GetComponent<AbstractBoard>();
return new Match3Sensor(board, ObservationType, SensorName);
}
/// <inheritdoc/>
public override int[] GetObservationShape()
{
var board = GetComponent<AbstractBoard>();
if (board == null)
{
return System.Array.Empty<int>();
}
var specialSize = board.NumSpecialTypes == 0 ? 0 : board.NumSpecialTypes + 1;
return ObservationType == Match3ObservationType.Vector ?
new[] { board.Rows * board.Columns * (board.NumCellTypes + specialSize) } :
new[] { board.Rows, board.Columns, board.NumCellTypes + specialSize };
}
}
}

3
com.unity.ml-agents.extensions/Runtime/Match3/Match3SensorComponent.cs.meta


fileFormatVersion: 2
guid: 530d2f105aa145bd8a00e021bdd925fd
timeCreated: 1600906676

260
com.unity.ml-agents.extensions/Runtime/Match3/Move.cs


using System;
namespace Unity.MLAgents.Extensions.Match3
{
/// <summary>
/// Directions for a Move.
/// </summary>
public enum Direction
{
/// <summary>
/// Move up (increasing row direction).
/// </summary>
Up,
/// <summary>
/// Move down (decreasing row direction).
/// </summary>
Down, // -row direction
/// <summary>
/// Move left (decreasing column direction).
/// </summary>
Left, // -column direction
/// <summary>
/// Move right (increasing column direction).
/// </summary>
Right, // +column direction
}
/// <summary>
/// Struct that encapsulates a swap of adjacent cells.
/// A Move can be constructed from either a starting row, column, and direction,
/// or from a "move index" between 0 and NumPotentialMoves()-1.
/// Moves are enumerated as the internal edges of the game grid.
/// Left/right moves come first. There are (maxCols - 1) * maxRows of these.
/// Up/down moves are next. There are (maxRows - 1) * maxCols of these.
/// </summary>
public struct Move
{
/// <summary>
/// Index of the move, from 0 to NumPotentialMoves-1.
/// </summary>
public int MoveIndex;
/// <summary>
/// Row of the cell that will be moved.
/// </summary>
public int Row;
/// <summary>
/// Column of the cell that will be moved.
/// </summary>
public int Column;
/// <summary>
/// Direction that the cell will be moved.
/// </summary>
public Direction Direction;
/// <summary>
/// Construct a Move from its move index and the board size.
/// This is useful for iterating through all the Moves on a board, or constructing
/// the Move corresponding to an Agent decision.
/// </summary>
/// <param name="moveIndex">Must be between 0 and NumPotentialMoves(maxRows, maxCols).</param>
/// <param name="maxRows"></param>
/// <param name="maxCols"></param>
/// <returns></returns>
/// <exception cref="ArgumentOutOfRangeException"></exception>
public static Move FromMoveIndex(int moveIndex, int maxRows, int maxCols)
{
if (moveIndex < 0 || moveIndex >= NumPotentialMoves(maxRows, maxCols))
{
throw new ArgumentOutOfRangeException("Invalid move index.");
}
Direction dir;
int row, col;
if (moveIndex < (maxCols - 1) * maxRows)
{
dir = Direction.Right;
col = moveIndex % (maxCols - 1);
row = moveIndex / (maxCols - 1);
}
else
{
dir = Direction.Up;
var offset = moveIndex - (maxCols - 1) * maxRows;
col = offset % maxCols;
row = offset / maxCols;
}
return new Move
{
MoveIndex = moveIndex,
Direction = dir,
Row = row,
Column = col
};
}
/// <summary>
/// Increment the Move to the next MoveIndex, and update the Row, Column, and Direction accordingly.
/// </summary>
/// <param name="maxRows"></param>
/// <param name="maxCols"></param>
public void Next(int maxRows, int maxCols)
{
var switchoverIndex = (maxCols - 1) * maxRows;
MoveIndex++;
if (MoveIndex < switchoverIndex)
{
Column++;
if (Column == maxCols - 1)
{
Row++;
Column = 0;
}
}
else if (MoveIndex == switchoverIndex)
{
// switch from moving right to moving up
Row = 0;
Column = 0;
Direction = Direction.Up;
}
else
{
Column++;
if (Column == maxCols)
{
Row++;
Column = 0;
}
}
}
/// <summary>
/// Construct a Move from the row, column, and direction.
/// </summary>
/// <param name="row"></param>
/// <param name="col"></param>
/// <param name="dir"></param>
/// <param name="maxRows"></param>
/// <param name="maxCols"></param>
/// <returns></returns>
public static Move FromPositionAndDirection(int row, int col, Direction dir, int maxRows, int maxCols)
{
// Check for out-of-bounds
if (row < 0 || row >= maxRows)
{
throw new IndexOutOfRangeException($"row was {row}, but must be between 0 and {maxRows - 1}.");
}
if (col < 0 || col >= maxCols)
{
throw new IndexOutOfRangeException($"col was {col}, but must be between 0 and {maxCols - 1}.");
}
// Check moves that would go out of bounds e.g. col == 0 and dir == Left
if (
row == 0 && dir == Direction.Down ||
row == maxRows - 1 && dir == Direction.Up ||
col == 0 && dir == Direction.Left ||
col == maxCols - 1 && dir == Direction.Right
)
{
throw new IndexOutOfRangeException($"Cannot move cell at row={row} col={col} in Direction={dir}");
}
// Normalize - only consider Right and Up
if (dir == Direction.Left)
{
dir = Direction.Right;
col = col - 1;
}
else if (dir == Direction.Down)
{
dir = Direction.Up;
row = row - 1;
}
int moveIndex;
if (dir == Direction.Right)
{
moveIndex = col + row * (maxCols - 1);
}
else
{
var offset = (maxCols - 1) * maxRows;
moveIndex = offset + col + row * maxCols;
}
return new Move
{
Row = row,
Column = col,
Direction = dir,
MoveIndex = moveIndex,
};
}
/// <summary>
/// Get the other row and column that correspond to this move.
/// </summary>
/// <returns></returns>
/// <exception cref="ArgumentOutOfRangeException"></exception>
public (int Row, int Column) OtherCell()
{
switch (Direction)
{
case Direction.Up:
return (Row + 1, Column);
case Direction.Down:
return (Row - 1, Column);
case Direction.Left:
return (Row, Column - 1);
case Direction.Right:
return (Row, Column + 1);
default:
throw new ArgumentOutOfRangeException();
}
}
/// <summary>
/// Get the opposite direction of this move.
/// </summary>
/// <returns></returns>
/// <exception cref="ArgumentOutOfRangeException"></exception>
public Direction OtherDirection()
{
switch (Direction)
{
case Direction.Up:
return Direction.Down;
case Direction.Down:
return Direction.Up;
case Direction.Left:
return Direction.Right;
case Direction.Right:
return Direction.Left;
default:
throw new ArgumentOutOfRangeException();
}
}
/// <summary>
/// Return the number of potential moves for a board of the given size.
/// This is equivalent to the number of internal edges in the board.
/// </summary>
/// <param name="maxRows"></param>
/// <param name="maxCols"></param>
/// <returns></returns>
public static int NumPotentialMoves(int maxRows, int maxCols)
{
return maxRows * (maxCols - 1) + (maxRows - 1) * (maxCols);
}
}
}

3
com.unity.ml-agents.extensions/Runtime/Match3/Move.cs.meta


fileFormatVersion: 2
guid: 41d6d7b9e07c4ef1ae075c74a906906b
timeCreated: 1600466100

152
com.unity.ml-agents.extensions/Tests/Editor/Match3/AbstractBoardTests.cs


using System;
using System.Collections.Generic;
using UnityEngine;
using NUnit.Framework;
using Unity.MLAgents.Extensions.Match3;
namespace Unity.MLAgents.Extensions.Tests.Match3
{
internal class StringBoard : AbstractBoard
{
private string[] m_Board;
private string[] m_Special;
/// <summary>
/// Convert a string like "000\n010\n000" to a board representation
/// Row 0 is considered the bottom row
/// </summary>
/// <param name="newBoard"></param>
public void SetBoard(string newBoard)
{
m_Board = newBoard.Split((char[])null, StringSplitOptions.RemoveEmptyEntries);
Rows = m_Board.Length;
Columns = m_Board[0].Length;
NumCellTypes = 0;
for (var r = 0; r < Rows; r++)
{
for (var c = 0; c < Columns; c++)
{
NumCellTypes = Mathf.Max(NumCellTypes, 1 + GetCellType(r, c));
}
}
}
public void SetSpecial(string newSpecial)
{
m_Special = newSpecial.Split((char[])null, StringSplitOptions.RemoveEmptyEntries);
Debug.Assert(Rows == m_Special.Length);
Debug.Assert(Columns == m_Special[0].Length);
NumSpecialTypes = 0;
for (var r = 0; r < Rows; r++)
{
for (var c = 0; c < Columns; c++)
{
NumSpecialTypes = Mathf.Max(NumSpecialTypes, GetSpecialType(r, c));
}
}
}
public override bool MakeMove(Move m)
{
return true;
}
public override bool IsMoveValid(Move m)
{
return SimpleIsMoveValid(m);
}
public override int GetCellType(int row, int col)
{
var character = m_Board[m_Board.Length - 1 - row][col];
return (int)(character - '0');
}
public override int GetSpecialType(int row, int col)
{
var character = m_Special[m_Board.Length - 1 - row][col];
return (int)(character - '0');
}
}
public class AbstractBoardTests
{
[Test]
public void TestBoardInit()
{
var boardString =
@"000
000
010";
var gameObj = new GameObject("board");
var board = gameObj.AddComponent<StringBoard>();
board.SetBoard(boardString);
Assert.AreEqual(3, board.Rows);
Assert.AreEqual(3, board.Columns);
Assert.AreEqual(2, board.NumCellTypes);
for (var r = 0; r < 3; r++)
{
for (var c = 0; c < 3; c++)
{
var expected = (r == 0 && c == 1) ? 1 : 0;
Assert.AreEqual(expected, board.GetCellType(r, c));
}
}
}
[Test]
public void TestCheckValidMoves()
{
var gameObj = new GameObject("board");
var board = gameObj.AddComponent<StringBoard>();
var boardString =
@"0105
1024
0203
2022";
board.SetBoard(boardString);
var validMoves = new[]
{
Move.FromPositionAndDirection(2, 1, Direction.Up, board.Rows, board.Columns), // equivalent to (3, 1, Down)
Move.FromPositionAndDirection(2, 1, Direction.Left, board.Rows, board.Columns), // equivalent to (2, 0, Right)
Move.FromPositionAndDirection(2, 1, Direction.Down, board.Rows, board.Columns), // equivalent to (1, 1, Up)
Move.FromPositionAndDirection(2, 1, Direction.Right, board.Rows, board.Columns),
Move.FromPositionAndDirection(1, 1, Direction.Down, board.Rows, board.Columns),
Move.FromPositionAndDirection(1, 1, Direction.Left, board.Rows, board.Columns),
Move.FromPositionAndDirection(1, 1, Direction.Right, board.Rows, board.Columns),
Move.FromPositionAndDirection(0, 1, Direction.Left, board.Rows, board.Columns),
};
foreach (var m in validMoves)
{
Assert.IsTrue(board.IsMoveValid(m));
}
// Run through all moves and make sure those are the only valid ones
HashSet<int> validIndices = new HashSet<int>();
foreach (var m in validMoves)
{
validIndices.Add(m.MoveIndex);
}
foreach (var move in board.AllMoves())
{
var expected = validIndices.Contains(move.MoveIndex);
Assert.AreEqual(expected, board.IsMoveValid(move), $"({move.Row}, {move.Column}, {move.Direction})");
}
HashSet<int> validIndicesFromIterator = new HashSet<int>();
foreach (var move in board.ValidMoves())
{
validIndicesFromIterator.Add(move.MoveIndex);
}
Assert.IsTrue(validIndices.SetEquals(validIndicesFromIterator));
}
}
}

3
com.unity.ml-agents.extensions/Tests/Editor/Match3/AbstractBoardTests.cs.meta


fileFormatVersion: 2
guid: a6d0404471364cd5b0b86ef72e6fe653
timeCreated: 1601332740

115
com.unity.ml-agents.extensions/Tests/Editor/Match3/Match3ActuatorTests.cs


using NUnit.Framework;
using Unity.MLAgents.Extensions.Match3;
using UnityEngine;
namespace Unity.MLAgents.Extensions.Tests.Match3
{
internal class SimpleBoard : AbstractBoard
{
public int LastMoveIndex;
public bool MovesAreValid = true;
public bool CallbackCalled;
public override int GetCellType(int row, int col)
{
return 0;
}
public override int GetSpecialType(int row, int col)
{
return 0;
}
public override bool IsMoveValid(Move m)
{
return MovesAreValid;
}
public override bool MakeMove(Move m)
{
LastMoveIndex = m.MoveIndex;
return MovesAreValid;
}
public void Callback()
{
CallbackCalled = true;
}
}
public class Match3ActuatorTests
{
[SetUp]
public void SetUp()
{
if (Academy.IsInitialized)
{
Academy.Instance.Dispose();
}
}
[TestCase(true)]
[TestCase(false)]
public void TestValidMoves(bool movesAreValid)
{
// Check that a board with no valid moves doesn't raise an exception.
var gameObj = new GameObject();
var board = gameObj.AddComponent<SimpleBoard>();
var agent = gameObj.AddComponent<Agent>();
gameObj.AddComponent<Match3ActuatorComponent>();
board.Rows = 5;
board.Columns = 5;
board.NumCellTypes = 5;
board.NumSpecialTypes = 0;
board.MovesAreValid = movesAreValid;
board.OnNoValidMovesAction = board.Callback;
board.LastMoveIndex = -1;
agent.LazyInitialize();
agent.RequestDecision();
Academy.Instance.EnvironmentStep();
if (movesAreValid)
{
Assert.IsFalse(board.CallbackCalled);
}
else
{
Assert.IsTrue(board.CallbackCalled);
}
Assert.AreNotEqual(-1, board.LastMoveIndex);
}
[Test]
public void TestActionSpec()
{
var gameObj = new GameObject();
var board = gameObj.AddComponent<SimpleBoard>();
var actuator = gameObj.AddComponent<Match3ActuatorComponent>();
board.Rows = 5;
board.Columns = 5;
board.NumCellTypes = 5;
board.NumSpecialTypes = 0;
var actionSpec = actuator.ActionSpec;
Assert.AreEqual(1, actionSpec.NumDiscreteActions);
Assert.AreEqual(board.NumMoves(), actionSpec.BranchSizes[0]);
}
[Test]
public void TestActionSpecNullBoard()
{
var gameObj = new GameObject();
var actuator = gameObj.AddComponent<Match3ActuatorComponent>();
var actionSpec = actuator.ActionSpec;
Assert.AreEqual(0, actionSpec.NumDiscreteActions);
Assert.AreEqual(0, actionSpec.NumContinuousActions);
}
}
}

3
com.unity.ml-agents.extensions/Tests/Editor/Match3/Match3ActuatorTests.cs.meta


fileFormatVersion: 2
guid: 2edf24df24ac426085cb31a94d063683
timeCreated: 1603392289

314
com.unity.ml-agents.extensions/Tests/Editor/Match3/Match3SensorTests.cs


using System;
using System.Collections.Generic;
using System.IO;
using NUnit.Framework;
using Unity.MLAgents.Actuators;
using Unity.MLAgents.Extensions.Match3;
using UnityEngine;
using Unity.MLAgents.Extensions.Tests.Sensors;
using Unity.MLAgents.Sensors;
namespace Unity.MLAgents.Extensions.Tests.Match3
{
public class Match3SensorTests
{
// Whether the expected PNG data should be written to a file.
// Only set this to true if the compressed observation format changes.
private bool WritePNGDataToFile = false;
[Test]
public void TestVectorObservations()
{
var boardString =
@"000
000
010";
var gameObj = new GameObject("board");
var board = gameObj.AddComponent<StringBoard>();
board.SetBoard(boardString);
var sensorComponent = gameObj.AddComponent<Match3SensorComponent>();
sensorComponent.ObservationType = Match3ObservationType.Vector;
var sensor = sensorComponent.CreateSensor();
var expectedShape = new[] { 3 * 3 * 2 };
Assert.AreEqual(expectedShape, sensorComponent.GetObservationShape());
Assert.AreEqual(expectedShape, sensor.GetObservationShape());
var expectedObs = new float[]
{
1, 0, /**/ 0, 1, /**/ 1, 0,
1, 0, /**/ 1, 0, /**/ 1, 0,
1, 0, /**/ 1, 0, /**/ 1, 0,
};
SensorTestHelper.CompareObservation(sensor, expectedObs);
}
[Test]
public void TestVectorObservationsSpecial()
{
var boardString =
@"000
000
010";
var specialString =
@"010
200
000";
var gameObj = new GameObject("board");
var board = gameObj.AddComponent<StringBoard>();
board.SetBoard(boardString);
board.SetSpecial(specialString);
var sensorComponent = gameObj.AddComponent<Match3SensorComponent>();
sensorComponent.ObservationType = Match3ObservationType.Vector;
var sensor = sensorComponent.CreateSensor();
var expectedShape = new[] { 3 * 3 * (2 + 3) };
Assert.AreEqual(expectedShape, sensorComponent.GetObservationShape());
Assert.AreEqual(expectedShape, sensor.GetObservationShape());
var expectedObs = new float[]
{
1, 0, 1, 0, 0, /* (0, 0) */ 0, 1, 1, 0, 0, /* (0, 1) */ 1, 0, 1, 0, 0, /* (0, 0) */
1, 0, 0, 0, 1, /* (0, 2) */ 1, 0, 1, 0, 0, /* (0, 0) */ 1, 0, 1, 0, 0, /* (0, 0) */
1, 0, 1, 0, 0, /* (0, 0) */ 1, 0, 0, 1, 0, /* (0, 1) */ 1, 0, 1, 0, 0, /* (0, 0) */
};
SensorTestHelper.CompareObservation(sensor, expectedObs);
}
[Test]
public void TestVisualObservations()
{
var boardString =
@"000
000
010";
var gameObj = new GameObject("board");
var board = gameObj.AddComponent<StringBoard>();
board.SetBoard(boardString);
var sensorComponent = gameObj.AddComponent<Match3SensorComponent>();
sensorComponent.ObservationType = Match3ObservationType.UncompressedVisual;
var sensor = sensorComponent.CreateSensor();
var expectedShape = new[] { 3, 3, 2 };
Assert.AreEqual(expectedShape, sensorComponent.GetObservationShape());
Assert.AreEqual(expectedShape, sensor.GetObservationShape());
Assert.AreEqual(SensorCompressionType.None, sensor.GetCompressionType());
var expectedObs = new float[]
{
1, 0, /**/ 0, 1, /**/ 1, 0,
1, 0, /**/ 1, 0, /**/ 1, 0,
1, 0, /**/ 1, 0, /**/ 1, 0,
};
SensorTestHelper.CompareObservation(sensor, expectedObs);
var expectedObs3D = new float[,,]
{
{{1, 0}, {0, 1}, {1, 0}},
{{1, 0}, {1, 0}, {1, 0}},
{{1, 0}, {1, 0}, {1, 0}},
};
SensorTestHelper.CompareObservation(sensor, expectedObs3D);
}
[Test]
public void TestVisualObservationsSpecial()
{
var boardString =
@"000
000
010";
var specialString =
@"010
200
000";
var gameObj = new GameObject("board");
var board = gameObj.AddComponent<StringBoard>();
board.SetBoard(boardString);
board.SetSpecial(specialString);
var sensorComponent = gameObj.AddComponent<Match3SensorComponent>();
sensorComponent.ObservationType = Match3ObservationType.UncompressedVisual;
var sensor = sensorComponent.CreateSensor();
var expectedShape = new[] { 3, 3, 2 + 3 };
Assert.AreEqual(expectedShape, sensorComponent.GetObservationShape());
Assert.AreEqual(expectedShape, sensor.GetObservationShape());
Assert.AreEqual(SensorCompressionType.None, sensor.GetCompressionType());
var expectedObs = new float[]
{
1, 0, 1, 0, 0, /* (0, 0) */ 0, 1, 1, 0, 0, /* (0, 1) */ 1, 0, 1, 0, 0, /* (0, 0) */
1, 0, 0, 0, 1, /* (0, 2) */ 1, 0, 1, 0, 0, /* (0, 0) */ 1, 0, 1, 0, 0, /* (0, 0) */
1, 0, 1, 0, 0, /* (0, 0) */ 1, 0, 0, 1, 0, /* (0, 1) */ 1, 0, 1, 0, 0, /* (0, 0) */
};
SensorTestHelper.CompareObservation(sensor, expectedObs);
var expectedObs3D = new float[,,]
{
{{1, 0, 1, 0, 0}, {0, 1, 1, 0, 0}, {1, 0, 1, 0, 0}},
{{1, 0, 0, 0, 1}, {1, 0, 1, 0, 0}, {1, 0, 1, 0, 0}},
{{1, 0, 1, 0, 0}, {1, 0, 0, 1, 0}, {1, 0, 1, 0, 0}},
};
SensorTestHelper.CompareObservation(sensor, expectedObs3D);
}
[Test]
public void TestCompressedVisualObservations()
{
var boardString =
@"000
000
010";
var gameObj = new GameObject("board");
var board = gameObj.AddComponent<StringBoard>();
board.SetBoard(boardString);
var sensorComponent = gameObj.AddComponent<Match3SensorComponent>();
sensorComponent.ObservationType = Match3ObservationType.CompressedVisual;
var sensor = sensorComponent.CreateSensor();
var expectedShape = new[] { 3, 3, 2 };
Assert.AreEqual(expectedShape, sensorComponent.GetObservationShape());
Assert.AreEqual(expectedShape, sensor.GetObservationShape());
Assert.AreEqual(SensorCompressionType.PNG, sensor.GetCompressionType());
var pngData = sensor.GetCompressedObservation();
if (WritePNGDataToFile)
{
// Enable this if the format of the observation changes
SavePNGs(pngData, "match3obs");
}
var expectedPng = LoadPNGs("match3obs", 1);
Assert.AreEqual(expectedPng, pngData);
}
[Test]
public void TestCompressedVisualObservationsSpecial()
{
var boardString =
@"000
000
010";
var specialString =
@"010
200
000";
var gameObj = new GameObject("board");
var board = gameObj.AddComponent<StringBoard>();
board.SetBoard(boardString);
board.SetSpecial(specialString);
var sensorComponent = gameObj.AddComponent<Match3SensorComponent>();
sensorComponent.ObservationType = Match3ObservationType.CompressedVisual;
var sensor = sensorComponent.CreateSensor();
var expectedShape = new[] { 3, 3, 2 + 3 };
Assert.AreEqual(expectedShape, sensorComponent.GetObservationShape());
Assert.AreEqual(expectedShape, sensor.GetObservationShape());
Assert.AreEqual(SensorCompressionType.PNG, sensor.GetCompressionType());
var concatenatedPngData = sensor.GetCompressedObservation();
var pathPrefix = "match3obs_special";
if (WritePNGDataToFile)
{
// Enable this if the format of the observation changes
SavePNGs(concatenatedPngData, pathPrefix);
}
var expectedPng = LoadPNGs(pathPrefix, 2);
Assert.AreEqual(expectedPng, concatenatedPngData);
}
/// <summary>
/// Helper method for un-concatenating PNG observations.
/// </summary>
/// <param name="concatenated"></param>
/// <returns></returns>
List<byte[]> SplitPNGs(byte[] concatenated)
{
var pngsOut = new List<byte[]>();
var pngHeader = new byte[] { 137, 80, 78, 71, 13, 10, 26, 10 };
var current = new List<byte>();
for (var i = 0; i < concatenated.Length; i++)
{
current.Add(concatenated[i]);
// Check if the header starts at the next position
// If so, we'll start a new output array.
var headerIsNext = false;
if (i + 1 < concatenated.Length - pngHeader.Length)
{
for (var j = 0; j < pngHeader.Length; j++)
{
if (concatenated[i + 1 + j] != pngHeader[j])
{
break;
}
if (j == pngHeader.Length - 1)
{
headerIsNext = true;
}
}
}
if (headerIsNext)
{
pngsOut.Add(current.ToArray());
current = new List<byte>();
}
}
pngsOut.Add(current.ToArray());
return pngsOut;
}
void SavePNGs(byte[] concatenatedPngData, string pathPrefix)
{
var splitPngs = SplitPNGs(concatenatedPngData);
for (var i = 0; i < splitPngs.Count; i++)
{
var pngData = splitPngs[i];
var path = $"Packages/com.unity.ml-agents.extensions/Tests/Editor/Match3/{pathPrefix}{i}.png";
using (var sw = File.Create(path))
{
foreach (var b in pngData)
{
sw.WriteByte(b);
}
}
}
}
byte[] LoadPNGs(string pathPrefix, int numExpected)
{
var bytesOut = new List<byte>();
for (var i = 0; i < numExpected; i++)
{
var path = $"Packages/com.unity.ml-agents.extensions/Tests/Editor/Match3/{pathPrefix}{i}.png";
var res = File.ReadAllBytes(path);
bytesOut.AddRange(res);
}
return bytesOut.ToArray();
}
}
}

3
com.unity.ml-agents.extensions/Tests/Editor/Match3/Match3SensorTests.cs.meta


fileFormatVersion: 2
guid: dfe94a9d6e994f408cb97d07dd44c994
timeCreated: 1603493723

60
com.unity.ml-agents.extensions/Tests/Editor/Match3/MoveTests.cs


using System;
using NUnit.Framework;
using Unity.MLAgents.Extensions.Match3;
namespace Unity.MLAgents.Extensions.Tests.Match3
{
public class MoveTests
{
[Test]
public void TestMoveEquivalence()
{
var moveUp = Move.FromPositionAndDirection(1, 1, Direction.Up, 10, 10);
var moveDown = Move.FromPositionAndDirection(2, 1, Direction.Down, 10, 10);
Assert.AreEqual(moveUp.MoveIndex, moveDown.MoveIndex);
var moveRight = Move.FromPositionAndDirection(1, 1, Direction.Right, 10, 10);
var moveLeft = Move.FromPositionAndDirection(1, 2, Direction.Left, 10, 10);
Assert.AreEqual(moveRight.MoveIndex, moveLeft.MoveIndex);
}
[Test]
public void TestNext()
{
var maxRows = 8;
var maxCols = 13;
// make sure using Next agrees with FromMoveIndex.
var advanceMove = Move.FromMoveIndex(0, maxRows, maxCols);
for (var moveIndex = 0; moveIndex < Move.NumPotentialMoves(maxRows, maxCols); moveIndex++)
{
var moveFromIndex = Move.FromMoveIndex(moveIndex, maxRows, maxCols);
Assert.AreEqual(advanceMove.MoveIndex, moveFromIndex.MoveIndex);
Assert.AreEqual(advanceMove.Row, moveFromIndex.Row);
Assert.AreEqual(advanceMove.Column, moveFromIndex.Column);
Assert.AreEqual(advanceMove.Direction, moveFromIndex.Direction);
advanceMove.Next(maxRows, maxCols);
}
}
// These are off the board
[TestCase(-1, 5, Direction.Up)]
[TestCase(10, 5, Direction.Up)]
[TestCase(5, -1, Direction.Up)]
[TestCase(5, 10, Direction.Up)]
// These are on the board but would move off
[TestCase(0, 5, Direction.Down)]
[TestCase(9, 5, Direction.Up)]
[TestCase(5, 0, Direction.Left)]
[TestCase(5, 9, Direction.Right)]
public void TestInvalidMove(int row, int col, Direction dir)
{
int numRows = 10, numCols = 10;
Assert.Throws<IndexOutOfRangeException>(() =>
{
Move.FromPositionAndDirection(row, col, dir, numRows, numCols);
});
}
}
}

3
com.unity.ml-agents.extensions/Tests/Editor/Match3/MoveTests.cs.meta


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