/// <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);
}
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)}");
}
}
public void CreateMoveMaker()
{
//TODO: Garbage generation here
var moveMakers = new IMoveDecisionMaker[SimulationState.EndLocation];
for (var i = 0; i < SimulationState.EndLocation; i++)
{
var offset = i * 8;
moveMakers[i] = new TuneableUtilityMoveMaker(
Gene[offset + 0],
Gene[offset + 1],
Gene[offset + 2],
Gene[offset + 3],
Gene[offset + 4],
Gene[offset + 5],
Gene[offset + 6],
Gene[offset + 7]
);
}
MoveMaker = new RangeSplitByBoardPositionDelegationMoveMaker(moveMakers);
}
public MoveOnlyMonteCarloTreeSearchWithPreplacerMoveMaker(int iterations, IMoveDecisionMaker rolloutMoveMaker, PreplacerStrategy preplacer) : base(iterations, rolloutMoveMaker)
{
_preplacer = preplacer;
}
public MonteCarloTreeSearchPlacementExpander(int iterations, IMoveDecisionMaker rolloutMoveMaker, IBoardEvaluator boardEvaluator, int maxChildrenPerPiece) : base(iterations, rolloutMoveMaker)
{
MaxChildrenPerPiece = maxChildrenPerPiece;
BoardEvaluator = boardEvaluator;
}
public MonteCarloTreeSearchMoveMaker(int iterations, IMoveDecisionMaker rolloutMoveMaker, IBoardEvaluator boardEvaluator, int maxChildrenPerPiece)
{
_mcts = new MonteCarloTreeSearchPlacementExpander(iterations, rolloutMoveMaker, boardEvaluator, maxChildrenPerPiece);
}
protected BaseMoveOnlyMonteCarloTreeSearchMoveMaker(int iterations, IMoveDecisionMaker rolloutMoveMaker = null)
{
Mcts = new MonteCarloTreeSearch <SearchNode>(iterations, rolloutMoveMaker);
}
public MoveOnlyMonteCarloTreeSearchMoveMaker(int iterations, IMoveDecisionMaker rolloutMoveMaker = null, bool useMinusOne = false, double progressiveBiasWeight = 0) : base(iterations, rolloutMoveMaker)
{
_useMinusOne = useMinusOne;
_progressiveBiasWeight = progressiveBiasWeight;
}
public PlayerDecisionMaker(IMoveDecisionMaker moveDecisionMaker, IPlacementDecisionMaker placementDecisionMaker)
{
MoveDecisionMaker = moveDecisionMaker;
PlacementDecisionMaker = placementDecisionMaker;
}
public MonteCarloTreeSearch(int iterations, IMoveDecisionMaker rolloutMoveMaker)
{
Iterations = iterations;
RolloutMoveMaker = rolloutMoveMaker ?? new RandomMoveMaker(0);
}
