Building a board game with the TFLite plugin for Flutter

by
Tensorflow

Posted by Wei Wei, Developer Advocate

In our previous blog posts Building a board game app with TensorFlow: a new TensorFlow Lite reference app and Building a reinforcement learning agent with JAX, and deploying it on Android with TensorFlow Lite, we demonstrated how to train a reinforcement learning (RL) agent with TensorFlow, TensorFlow Agents and JAX respectively, and then deploy the converted TFLite model in an Android app using TensorFlow Lite, to play a simple board game ‘Plane Strike’.

While these end-to-end tutorials are helpful for Android developers, we have heard from the Flutter developer community that it would be interesting to make the app cross-platform. Inspired by the officially released TensorFlow Lite Plugin for Flutter recently, we are going to write one last tutorial and port the app to Flutter.
Flow Chart illustrating training a Reinforncement Learning (RL) Agent with TensorFlow, TensorFlow Agents and JAX, deploying the converted model in an Android app and Flutter using the TensorFlow Lite plugin

Since we already have the model trained with TensorFlow and converted to TFLite, we can just load the model with TFLite interpreter:

void _loadModel() async {
  // Create the interpreter
  _interpreter = await Interpreter.fromAsset(_modelFile);
}

Then we pass in the user board state and help the game agent identify the most promising position to strike next (please refer to our previous blog posts if you need a refresher on the game rules) by running TFLite inference:

int predict(List<List<double>> boardState) {
  var input = [boardState];
  var output = List.filled(_boardSize * _boardSize, 0)
      .reshape([1, _boardSize * _boardSize]);

  // Run inference
  _interpreter.run(input, output);

  // Argmax
  double max = output[0][0
];


  int
maxIdx = 0;
  for (int i = 1; i < _boardSize * _boardSize; i++) {
    if (max < output[0][i]) {
      maxIdx = i;
      max = output[0][i];
    }
  }

  return maxIdx;
}

That’s it! With some additional Flutter frontend code to render the game boards and track game progress, we can immediately run the game on both Android and iOS (currently the plugin only supports these two mobile platforms). You can find the complete code on GitHub.

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If you want to dig digger, there are a couple of things you can try:
  1. Convert the TFAgents-trained model to TFLite and run it with the plugin
  2. Leverage the RL technique we have used and build a new agent for the tic tac toe game in the Flutter Casual Games Toolkit. You will need to create a new RL environment and train the model from scratch before deployment, but the core concept and technique are pretty much the same.

This concludes this mini-series of blogs on leveraging TensorFlow/JAX to build games for Android and Flutter. And we very much look forward to all the exciting things you build with our tooling, so be sure to share them with @googledevs, @TensorFlow, and your developer communities!

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