/// <summary>
/// This method will let the AI play the maze for one game (windowless)
/// </summary>
public MazeCycleOutcome Travel(int timerTimeout = 5000)
{
MazeGame game = new MazeGame();
game.traveler = this;
game.InitGame(maze);
MazeCycleOutcome outcome = new MazeCycleOutcome();
// Start AI Training
currentSessionID = rlmNet.SessionStart();
SessionStarted?.Invoke(rlmNet.RandomnessCurrentValue);
tmr.Interval = timerTimeout;
tmr.Start();
timeout = 0;
int movesCnt = 0;
while (!outcome.GameOver && timeout == 0)
{
// set up input for x and y based on our current location on the maze
var inputs = new List <RlmIOWithValue>()
{
new RlmIOWithValue(rlmNet.Inputs.First(a => a.Name == "X"), game.traveler.location.X.ToString()),
new RlmIOWithValue(rlmNet.Inputs.First(a => a.Name == "Y"), game.traveler.location.Y.ToString()),
};
// get AI output based on inputs
RlmCycle cycle = new RlmCycle();
var aiResult = cycle.RunCycle(rlmNet, currentSessionID, inputs, Learn);
var direction = Convert.ToInt16(aiResult.CycleOutput.Outputs.First().Value);
// make the move
outcome = game.CycleMaze(direction);
// score AI output
double score = ScoreAI(outcome, game.traveler);
rlmNet.ScoreCycle(aiResult.CycleOutput.CycleID, score);
MazeCycleComplete?.Invoke(game.traveler.location.X, game.traveler.location.Y, outcome.BumpedIntoWall);
movesCnt++;
}
tmr.Stop();
// compute final score
outcome.FinalScore = game.CalculateFinalScore(game.Moves);
// End AI Training
rlmNet.SessionEnd(outcome.FinalScore);
SessionComplete?.Invoke(rlmNet.SessionCount, outcome.FinalScore, movesCnt);
return(outcome);
}
//public double Travel(ref int timeout, out int movesCnt)
public double Travel(int timerTimeout, out int movesCnt)
{
MazeGame game = new MazeGame();
game.InitGame(maze);
game.traveler = this;
game.traveler.location.X = maze.StartingPosition.X;
game.traveler.location.Y = maze.StartingPosition.Y;
var recentOutcome = new MazeCycleOutcome();
tmr.Interval = timerTimeout;
tmr.Start();
timeout = 0;
movesCnt = 0;
while (!recentOutcome.GameOver && timeout == 0)
{
movesCnt++;
var input = new BasicMLData(2);
input[0] = xInput.Normalize(Convert.ToDouble(game.traveler.location.X));
input[1] = yInput.Normalize(Convert.ToDouble(game.traveler.location.Y));
IMLData output = network.Compute(input);
double maxVal = double.MinValue;
int direction = 0;
for (int i = 0; i < output.Count; i++)
{
if (output[i] > maxVal)
{
direction = i;
maxVal = output[i];
}
}
recentOutcome = game.CycleMaze(direction);
MazeCycleComplete?.Invoke(game.traveler.location.X, game.traveler.location.Y, recentOutcome.BumpedIntoWall);
}
tmr.Stop();
var score = game.CalculateFinalScore(movesCnt);
return(score);
}
