public void AlwaysAdvanceToEndNoPiecePlacing()
{
var state = new SimulationState(SimulationHelpers.GetRandomPieces(), 0);
state.Fidelity = SimulationFidelity.NoPiecePlacing;
var runner = new SimulationRunner(state, new PlayerDecisionMaker(AlwaysAdvanceMoveMaker.Instance, null), new PlayerDecisionMaker(AlwaysAdvanceMoveMaker.Instance, null));
//The game definitely ends within 200 steps
for (var i = 0; i < 200 && !state.GameHasEnded; i++)
{
runner.PerformNextStep();
Assert.InRange(state.PlayerPosition[0], 0, SimulationState.EndLocation);
Assert.InRange(state.PlayerPosition[1], 0, SimulationState.EndLocation);
}
//Both players should be at the end
Assert.Equal(SimulationState.EndLocation, state.PlayerPosition[0]);
Assert.Equal(SimulationState.EndLocation, state.PlayerPosition[1]);
//Game should have ended
Assert.True(state.GameHasEnded);
//Players should have one button for each place they moved
Assert.Equal(SimulationState.EndLocation + SimulationState.PlayerStartingButtons, state.PlayerButtonAmount[0]);
Assert.Equal(SimulationState.EndLocation + SimulationState.PlayerStartingButtons, state.PlayerButtonAmount[1]);
//non-starting player should have won by collecting all of the leather patches (https://boardgamegeek.com/thread/1703957/how-break-stalemate)
Assert.Equal(1, state.WinningPlayer);
//Check the ending points are correct
Assert.Equal(SimulationState.EndLocation - BoardState.Width * BoardState.Height * 2 + SimulationState.PlayerStartingButtons, state.CalculatePlayerEndGameWorth(0));
Assert.Equal(SimulationState.EndLocation - BoardState.Width * BoardState.Height * 2 + SimulationState.PlayerStartingButtons + 2 * SimulationState.LeatherPatches.Length, state.CalculatePlayerEndGameWorth(1));
}
/// <summary>
/// Returns the amount of wins the challenger got (from 100)
/// </summary>
private static int CalculateChallengerWinsFrom100(IMoveDecisionMaker best, IMoveDecisionMaker challenger)
{
int challengerWins = 0;
Parallel.For(0, 200, (i) =>
{
var state = new SimulationState(SimulationHelpers.GetRandomPieces(i / 2), 0);
state.ActivePlayer = i % 2;
state.Fidelity = SimulationFidelity.NoPiecePlacing;
//TODO: May need a cheaper placement engine
var runner = new SimulationRunner(state,
new PlayerDecisionMaker(best, null),
new PlayerDecisionMaker(challenger, null));
while (!state.GameHasEnded)
{
runner.PerformNextStep();
}
if (state.WinningPlayer == 1)
{
Interlocked.Increment(ref challengerWins);
}
});
return(challengerWins);
}
public void Run()
{
Evaluate();
var req = new TrainRequest();
for (var g = 0; g < 1000; g++)
{
var timer = Stopwatch.StartNew();
//Parallel.For(0, 8, (i) =>
for (var i = 0; i < 8; i++)
{
Console.WriteLine("Starting game " + i);
var p0Samples = new List <TrainSample>();
var p1Samples = new List <TrainSample>();
var state = new SimulationState(SimulationHelpers.GetRandomPieces(), 0);
state.Fidelity = SimulationFidelity.NoPiecePlacing;
while (!state.GameHasEnded)
{
var sampleSet = state.ActivePlayer == 0 ? p0Samples : p1Samples;
var sample = _ai.MakeMoveWithResult(state);
sampleSet.Add(sample);
}
//Set the winner on the samples from the winner
var winningSamples = state.WinningPlayer == 0 ? p0Samples : p1Samples;
for (var j = 0; j < winningSamples.Count; j++)
{
winningSamples[j].IsWin = true;
}
//TODO: AddRange is terrible performance
lock (req)
{
req.Samples.AddRange(p0Samples);
req.Samples.AddRange(p1Samples);
}
} //);
timer.Stop();
Console.WriteLine("Took " + timer.ElapsedMilliseconds);
//if (req.Samples.Count >= 256)
{
Console.WriteLine("Training " + req.Samples.Count);
_client.Train(req);
req.Samples.Clear();
Evaluate();
}
}
Console.WriteLine("Hi");
Console.ReadLine();
}
private static void RunMoveMakerForPerformance()
{
var sw = Stopwatch.StartNew();
#if PERF_PARALLEL
Parallel.For(0, 10, (run) => //5900
#else
for (var run = 0; run < 4; run++) //13000
#endif
{
//var a = new PlayerDecisionMaker(new MoveOnlyMonteCarloTreeSearchMoveMaker(10000), PlacementMaker.FirstPossibleInstance);
//var b = new PlayerDecisionMaker(new MoveOnlyMonteCarloTreeSearchMoveMaker(5000), PlacementMaker.FirstPossibleInstance);
var mctsA = new MonteCarloTreeSearchMoveMaker(5000, TuneableUtilityMoveMaker.Tuning1, new TightBoardEvaluator(true), 2);
var a = new PlayerDecisionMaker(mctsA, new PlacementMaker(mctsA.PlacementStrategy));
var mctsB = new MonteCarloTreeSearchMoveMaker(5000, TuneableUtilityMoveMaker.Tuning1, new TightBoardEvaluator(true), 2);
var b = new PlayerDecisionMaker(mctsB, new PlacementMaker(mctsB.PlacementStrategy));
var state = new SimulationState(SimulationHelpers.GetRandomPieces(run / 2), 0);
var runner = new SimulationRunner(state, run % 2 == 0 ? a : b, run % 2 == 1 ? a : b);
while (!state.GameHasEnded)
{
runner.PerformNextStep();
}
Console.WriteLine(state.WinningPlayer);
}
public void BuyFirstPossibleMoveMakerNoPiecePlacing()
{
var state = new SimulationState(SimulationHelpers.GetRandomPieces(3), 0);
state.Fidelity = SimulationFidelity.NoPiecePlacing;
var runner = new SimulationRunner(state, new PlayerDecisionMaker(BuyFirstPossibleMoveMaker.Instance, null), new PlayerDecisionMaker(BuyFirstPossibleMoveMaker.Instance, null));
while (!state.GameHasEnded)
{
runner.PerformNextStep();
}
//Check someone bought something
Assert.True(state.Pieces.Count < PieceDefinition.AllPieceDefinitions.Length);
}
internal (SimulationState state, int gameId) CreateSimulation(int?randomSeed, int mctsIterations)
{
lock (this)
{
var id = ++_nextSim;
var state = new SimulationState(SimulationHelpers.GetRandomPieces(randomSeed), 0);
state.Fidelity = SimulationFidelity.NoPiecePlacing;
var opp = new MoveOnlyMonteCarloTreeSearchMoveMaker(mctsIterations);
_simulations[id] = state;
_opponents[id] = opp;
return(state, id);
}
}
private void TestStrategy(IPlacementStrategy strategy, int expectPiecesPlaced)
{
var pieces = new PieceCollection();
pieces.Populate(SimulationHelpers.GetRandomPieces(1));
int placed = 0;
var board = new BoardState();
for (var i = 0; i < pieces.Count; i++)
{
if (strategy.TryPlacePiece(board, PieceDefinition.AllPieceDefinitions[pieces[i]], in pieces, i + 1, out var bitmap, out var x, out var y))
{
placed++;
board.Place(bitmap, x, y);
}
public void BuyFirstPossibleMoveMakerPlacing()
{
var state = new SimulationState(SimulationHelpers.GetRandomPieces(1), 0);
var runner = new SimulationRunner(state, new PlayerDecisionMaker(BuyFirstPossibleMoveMaker.Instance, PlacementMaker.FirstPossibleInstance), new PlayerDecisionMaker(BuyFirstPossibleMoveMaker.Instance, PlacementMaker.FirstPossibleInstance));
while (!state.GameHasEnded)
{
runner.PerformNextStep();
}
//Check someone bought something
Assert.True(state.Pieces.Count < PieceDefinition.AllPieceDefinitions.Length);
Assert.Equal(11, state.PlayerButtonIncome[0]);
Assert.Equal(37, state.PlayerButtonAmount[0]);
}
static void TestPlacementOnly()
{
var stopwatch = Stopwatch.StartNew();
var strategies = new IPlacementStrategy[]
{
//FirstPossiblePlacementStrategy.Instance,
//SimpleClosestToWallAndCornerStrategy.Instance,
//ClosestToCornerLeastHolesTieBreakerPlacementStrategy.Instance,
//NextToPieceEdgeLeastHolesTieBreakerPlacementStrategy.Instance,
//TightPlacementStrategy.InstanceDoubler,
//TightPlacementStrategy.InstanceIncrement,
ExhaustiveMostFuturePlacementsPlacementStrategy.Instance1_1,
ExhaustiveMostFuturePlacementsPlacementStrategy.Instance1_6,
new BestEvaluatorStrategy(TuneablePattern2x2BoardEvaluator.HandTuned),
new BestEvaluatorStrategy(TuneablePattern2x2BoardEvaluator.Tuning1),
};
//foreach (var strategy in strategies)
Parallel.ForEach(strategies, (strategy) =>
{
//var rand = new Random(0);
int totalPlaced = 0;
for (var i = 0; i < 100; i++)
{
var pieces = new PieceCollection();
pieces.Populate(SimulationHelpers.GetRandomPieces(i));
int index = 0;
int placed = 0;
var board = new BoardState();
while (true)
{
var piece = pieces[index];
pieces.RemoveAt(index);
index = index % pieces.Count;
if (strategy.TryPlacePiece(board, PieceDefinition.AllPieceDefinitions[piece], in pieces, index, out var bitmap, out var x, out var y))
{
placed++;
board.Place(bitmap, x, y);
//Advance to a random one in the next 6 pieces (TODO: Would be good to bias this towards 1-3 as these are more likely)
//index = (index + rand.Next(0, 6)) % pieces.Count;
}
public void GetRandomPiecesPlaces0AtEnd()
{
for (var i = 0; i < 1000; i++)
{
var pieces = SimulationHelpers.GetRandomPieces(i);
//0 at end
Assert.Equal(0, pieces[pieces.Count - 1]);
//Array contains all pieces
pieces = pieces.OrderBy(p => p).ToList();
for (var j = 0; j < pieces.Count; j++)
{
Assert.Equal(j, pieces[j]);
}
}
}
public void AdvanceToEndPlacingSinglePatches()
{
var state = new SimulationState(SimulationHelpers.GetRandomPieces(), 0);
state.Fidelity = SimulationFidelity.FullSimulation;
var runner = new SimulationRunner(state, new PlayerDecisionMaker(AlwaysAdvanceMoveMaker.Instance, PlacementMaker.FirstPossibleInstance), new PlayerDecisionMaker(AlwaysAdvanceMoveMaker.Instance, PlacementMaker.FirstPossibleInstance));
while (!state.GameHasEnded)
{
runner.PerformNextStep();
}
//Both players should be at the end
Assert.Equal(SimulationState.EndLocation, state.PlayerPosition[0]);
Assert.Equal(SimulationState.EndLocation, state.PlayerPosition[1]);
//Game should have ended
Assert.True(state.GameHasEnded);
//Players should have one button for each place they moved
Assert.Equal(SimulationState.EndLocation + SimulationState.PlayerStartingButtons, state.PlayerButtonAmount[0]);
Assert.Equal(SimulationState.EndLocation + SimulationState.PlayerStartingButtons, state.PlayerButtonAmount[1]);
//non-starting player should have won by collecting all of the leather patches (https://boardgamegeek.com/thread/1703957/how-break-stalemate)
Assert.Equal(1, state.WinningPlayer);
//Check the ending points are correct
Assert.Equal(SimulationState.EndLocation - BoardState.Width * BoardState.Height * 2 + SimulationState.PlayerStartingButtons, state.CalculatePlayerEndGameWorth(0));
Assert.Equal(SimulationState.EndLocation - BoardState.Width * BoardState.Height * 2 + SimulationState.PlayerStartingButtons + 2 * SimulationState.LeatherPatches.Length, state.CalculatePlayerEndGameWorth(1));
//Check the pieces are on their board
int sum = 0;
for (var x = 0; x < BoardState.Width; x++)
{
for (var y = 0; y < BoardState.Height; y++)
{
if (state.PlayerBoardState[1][x, y])
{
sum++;
}
}
}
Assert.Equal(SimulationState.LeatherPatches.Length, sum);
}
private void Evaluate()
{
Console.WriteLine($"Evaluating vs " + _opp.Name);
var players = new IMoveDecisionMaker[] { _ai, _opp };
//Play X games (even amount), each player gets to start on each of the boards once
for (var game = 0; game < 4; game++)
{
var state = new SimulationState(SimulationHelpers.GetRandomPieces(game / 2), 0);
state.Fidelity = SimulationFidelity.NoPiecePlacing;
state.ActivePlayer = game % 2;
while (!state.GameHasEnded)
{
players[state.ActivePlayer].MakeMove(state);
}
Console.WriteLine($"Game {game}. Winner {state.WinningPlayer}. Scores: {state.CalculatePlayerEndGameWorth(0)} / {state.CalculatePlayerEndGameWorth(1)}");
}
}
static void TestFullAi()
{
var aiToTest = new Func <PlayerDecisionMaker>[]
{
//() => new PlayerDecisionMaker(new GreedyCardValueUtilityMoveMaker(-1), PlacementMaker.TightDoublerInstance),
//() => new PlayerDecisionMaker(new GreedyCardValueUtilityMoveMaker(0), PlacementMaker.TightDoublerInstance),
//() => new PlayerDecisionMaker(new GreedyCardValueUtilityMoveMaker(1), PlacementMaker.TightDoublerInstance),
//() => new PlayerDecisionMaker(new GreedyCardValueUtilityMoveMaker(2), PlacementMaker.TightDoublerInstance),
//
//() => new PlayerDecisionMaker(new GreedyCardValueUtilityMoveMaker(-1), PlacementMaker.ExhaustiveMostFuturePlacementsInstance1_6),
//() => new PlayerDecisionMaker(new GreedyCardValueUtilityMoveMaker(0), PlacementMaker.ExhaustiveMostFuturePlacementsInstance1_6),
//() => new PlayerDecisionMaker(new GreedyCardValueUtilityMoveMaker(1), PlacementMaker.ExhaustiveMostFuturePlacementsInstance1_6),
//() => new PlayerDecisionMaker(new GreedyCardValueUtilityMoveMaker(2), PlacementMaker.ExhaustiveMostFuturePlacementsInstance1_6),
//() => new PlayerDecisionMaker(new QuickRandomSearchMoveMaker(6, 1000), PlacementMaker.ExhaustiveMostFuturePlacementsInstance1_6),
//() => new PlayerDecisionMaker(new QuickRandomSearchMoveMaker(10, 2000), PlacementMaker.ExhaustiveMostFuturePlacementsInstance1_6),
//() => new PlayerDecisionMaker(new QuickRandomSearchMoveMaker(20, 5000), PlacementMaker.ExhaustiveMostFuturePlacementsInstance1_6),
//() => new PlayerDecisionMaker(new QuickRandomSearchMoveMaker(30, 10000), PlacementMaker.ExhaustiveMostFuturePlacementsInstance1_6),
//() => new PlayerDecisionMaker(new MoveOnlyMonteCarloTreeSearchMoveMaker(1000, TuneableUtilityMoveMaker.Tuning1), PlacementMaker.ExhaustiveMostFuturePlacementsInstance1_6),
() => new PlayerDecisionMaker(new MoveOnlyMonteCarloTreeSearchMoveMaker(10000, TuneableUtilityMoveMaker.Tuning1), PlacementMaker.ExhaustiveMostFuturePlacementsInstance1_6),
() => new PlayerDecisionMaker(new MoveOnlyMonteCarloTreeSearchMoveMaker(10000, TuneableUtilityMoveMaker.Tuning1), new PlacementMaker(new BestEvaluatorStrategy(TuneablePattern2x2BoardEvaluator.Tuning1))),
() =>
{
var p = new PreplacerStrategy(new EvaluatorTreeSearchPreplacer(TuneablePattern2x2BoardEvaluator.Tuning1, 4, 4, true));
var m = new MoveOnlyMonteCarloTreeSearchWithPreplacerMoveMaker(10000, TuneableUtilityMoveMaker.Tuning1, p);
return(new PlayerDecisionMaker(m, new PlacementMaker(p)));
},
() => new PlayerDecisionMaker(new AlphaBetaMoveMaker(15, TuneableByBoardPositionUtilityCalculator.Tuning1), PlacementMaker.ExhaustiveMostFuturePlacementsInstance1_6),
() => new PlayerDecisionMaker(new AlphaBetaMoveMaker(15, TuneableByBoardPositionUtilityCalculator.Tuning1), new PlacementMaker(new BestEvaluatorStrategy(TuneablePattern2x2BoardEvaluator.Tuning1))),
//
//() =>
//{
// var p = new PreplacerStrategy(new EvaluatorTreeSearchPreplacer(new Pattern2x2BoardEvaluator(), 4, 4, true));
// var m = new MoveOnlyMinimaxWithAlphaBetaPruningWithPreplacerMoveMaker(13, TuneableByBoardPositionUtilityCalculator.Tuning1, p);
// return new PlayerDecisionMaker(m, new PlacementMaker(p));
//},
() =>
{
var p = new PreplacerStrategy(new EvaluatorTreeSearchPreplacer(TuneablePattern2x2BoardEvaluator.Tuning1, 4, 4, true));
var m = new MoveOnlyAlphaBetaWithPreplacerMoveMaker(15, TuneableByBoardPositionUtilityCalculator.Tuning1, p);
return(new PlayerDecisionMaker(m, new PlacementMaker(p)));
},
};
const int TotalRuns = 500;
const bool enableConsoleLogging = false;
//TODO: Play each AI against each other AI 100 times and print a table of results
var totalWins = new int[aiToTest.Length, aiToTest.Length];
var totalTimeTaken = new long[aiToTest.Length];
Console.WriteLine($"Running {aiToTest.Length * (aiToTest.Length - 1) / 2} * {TotalRuns} Games");
int gameNumber = 0;
for (var a = 0; a < aiToTest.Length; a++)
{
for (var b = a; b < aiToTest.Length; b++)
{
if (a == b)
{
continue;
}
long aiATime = 0;
long aiBTime = 0;
var aiA = aiToTest[a];
var aiB = aiToTest[b];
Console.WriteLine($"{++gameNumber} {aiA().Name} vs {aiB().Name}");
Parallel.For(0, TotalRuns, new ParallelOptions {
MaxDegreeOfParallelism = 6
}, (run) =>
//for (var run = 0; run < TotalRuns; run++)
{
var state = new SimulationState(SimulationHelpers.GetRandomPieces(run / 2), 0);
ConsoleLogger logger = null;
if (enableConsoleLogging)
{
state.Logger = logger = new ConsoleLogger(state);
}
//state.Fidelity = SimulationFidelity.NoPiecePlacing;
//Let each Ai have half of the goes first and half second
var runner = new SimulationRunner(state, run % 2 == 0 ? aiA() : aiB(), run % 2 == 1 ? aiA() : aiB());
while (!state.GameHasEnded)
{
runner.PerformNextStep();
if (logger != null)
{
logger.PrintBoards(true);
}
}
var aWin = run % 2 == state.WinningPlayer;
lock (totalWins)
{
if (aWin)
{
totalWins[a, b]++;
}
else
{
totalWins[b, a]++;
}
}
Interlocked.Add(ref aiATime, runner.Stopwatches[run % 2].ElapsedMilliseconds);
Interlocked.Add(ref aiBTime, runner.Stopwatches[(run + 1) % 2].ElapsedMilliseconds);
//Console.WriteLine(aWin);
}
);
Console.WriteLine(totalWins[a, b]);
totalTimeTaken[a] += aiATime;
totalTimeTaken[b] += aiBTime;
}
}
//Calculate the total points for each
var total = Enumerable.Range(0, aiToTest.Length).Select(ai => Enumerable.Range(0, aiToTest.Length).Sum(opponent => totalWins[ai, opponent])).ToArray();
//Dump a CSV with the results
var filename = "result_" + DateTimeOffset.Now.Ticks + ".csv";
var res = new List <string>();
res.Add("," + string.Join(", ", aiToTest.Select(ai => ai().Name)) + ",Win%,Rank,Total Time");
for (var a = 0; a < aiToTest.Length; a++)
{
var line = aiToTest[a]().Name;
for (var b = 0; b < aiToTest.Length; b++)
{
if (a == b)
{
line += ",";
}
else
{
line += "," + totalWins[a, b];
}
}
//Win%, rank, time
line += "," + (total[a] / (float)(aiToTest.Length - 1) * 100 / TotalRuns).ToString("0.0");
line += "," + (aiToTest.Length - total.Count(c => c < total[a]));
line += "," + totalTimeTaken[a];
res.Add(line);
}
Console.WriteLine("Saving results as " + filename);
File.WriteAllLines(filename, res);
Process.Start(filename);
}