// Simulate real game to see how your bot will perform in real games.
private static async Task SimulatGamesScore <T>(ISmartBot <T> smartBot, int gameCount = 50) where T : LabeledDataPoint
{
Console.WriteLine("Simulating game.");
// TODO-Extra: Try using different combination bots (you can use random) to compares yours in different settings.
var bots = new IBot[]
{
smartBot,
smartBot,
smartBot,
smartBot,
};
var playerScores = new Dictionary <string, List <int> >()
{
{ "p1", new List <int>() },
{ "p2", new List <int>() },
{ "p3", new List <int>() },
{ "p4", new List <int>() },
};
for (int i = 0; i < gameCount; i++)
{
var simulation = await BomberjamRunner.StartSimulation(bots, false);
while (!simulation.IsFinished)
{
await simulation.ExecuteNextTick();
}
foreach (var player in simulation.CurrentState.Players)
{
playerScores[player.Key].Add(player.Value.score);
}
}
var avgP1 = playerScores["p1"].Aggregate((x, y) => x + y) / gameCount;
var avgP2 = playerScores["p2"].Aggregate((x, y) => x + y) / gameCount;
var avgP3 = playerScores["p3"].Aggregate((x, y) => x + y) / gameCount;
var avgP4 = playerScores["p4"].Aggregate((x, y) => x + y) / gameCount;
Console.WriteLine($"Average scores p1: {avgP1}");
Console.WriteLine($"Average scores p2: {avgP2}");
Console.WriteLine($"Average scores p3: {avgP3}");
Console.WriteLine($"Average scores p4: {avgP4}");
Console.WriteLine($"Average average: {(avgP1 + avgP2 + avgP3 + avgP4) / 4}");
}
public static void DoExam <T>(ISmartBot <T> smartBot) where T : LabeledDataPoint
{
smartBot.Load(modelSavePath);
var score = 0;
foreach (var data in ExamData)
{
var isInactive = smartBot.Predict(smartBot.ExtractDataPoint(data.Data));
if (isInactive == data.IsUnused)
{
++score;
}
}
Console.WriteLine($"Score: {score}/{ExamData.Count}");
}
private static async Task SimulateExample <T>(ISmartBot <T> smartBot) where T : LabeledDataPoint
{
await smartBot.Load(modelSavePath);
var bots = new IBot[]
{
smartBot,
smartBot,
smartBot,
smartBot
};
const bool saveGamelogFile = true;
var simulation = await BomberjamRunner.StartSimulation(bots, saveGamelogFile);
while (!simulation.IsFinished)
{
await simulation.ExecuteNextTick();
}
Console.WriteLine("Simulation completed!");
}
// Train, get metrics and save your Machine Learning Bot
private static async Task TrainAndSave <T>(ISmartBot <T> smartBot) where T : LabeledDataPoint
{
smartBot.Train(gameLogsPath);
await smartBot.Save(modelSavePath);
}
// Train, get metrics and save your Machine Learning Bot
private static async Task TestModel <T>(ISmartBot <T> smartBot) where T : LabeledDataPoint
{
smartBot.Train(gameLogsPath, true);
await SimulatGamesScore(smartBot);
}
// https://docs.microsoft.com/en-us/dotnet/machine-learning/how-to-guides/explain-machine-learning-model-permutation-feature-importance-ml-net
// Currently only support impact on the LightGbm
private static void EvaluateFeatures <T>(ISmartBot <T> smartBot) where T : LabeledDataPoint
{
smartBot.EvaluateFeatures(gameLogsPath);
}
private static async Task Game <T>(ISmartBot <T> smartBot) where T : LabeledDataPoint
{
await smartBot.Load(modelSavePath);
await PlayInBrowserExample(smartBot);
}
// https://docs.microsoft.com/en-us/dotnet/machine-learning/how-to-guides/explain-machine-learning-model-permutation-feature-importance-ml-net
// Currently only support impact LightGbm algo
public static void EvaluateFeatures <T>(ISmartBot <T> smartBot) where T : LabeledDataPoint
{
smartBot.EvaluateFeatures(dataPath);
}
public static async Task TestModel <T>(ISmartBot <T> smartBot) where T : LabeledDataPoint
{
smartBot.Train(dataPath, true);
await smartBot.Save(modelSavePath);
}
