/// <summary>
/// This method will let the AI play the maze for one game (windowless)
/// </summary>
public virtual MazeCycleOutcome Travel(int timerTimeout = 5000)
{
IMazeGame game = GetNewGameInstance();
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);
}
private MazeCycleOutcome Travel(bool learn, int?timerTimeout = null, CancellationToken?token = null, bool perMoveDisplay = false)
{
IMazeGame game = GetNewGameInstance();
MazeCycleOutcome outcome = new MazeCycleOutcome();
// Start AI Training
var currentSessionID = network.SessionStart();
SessionStarted?.Invoke(network.RandomnessCurrentValue);
recentMoves.Clear();
//tmr.Interval = timerTimeout;
//tmr.Start();
//timeout = 0;
int movesCnt = 1;
while (!outcome.GameOver) // && timeout == 0)
{
if (token?.IsCancellationRequested == true)
{
break;
}
// set up input for x and y based on our current location on the maze
var inputs = new List <RlmIOWithValue>()
{
//new RlmIOWithValue(network.Inputs.First(a => a.Name == "X"), game.traveler.location.X.ToString()),
//new RlmIOWithValue(network.Inputs.First(a => a.Name == "Y"), game.traveler.location.Y.ToString()),
new RlmIOWithValue(network.Inputs.First(a => a.Name == "Move"), movesCnt.ToString())
};
// get AI output based on inputs
RlmCycle cycle = new RlmCycle();
var aiResult = cycle.RunCycle(network, currentSessionID, inputs, learn);
var direction = Convert.ToInt16(aiResult.CycleOutput.Outputs.First().Value);
// make the move
outcome = game.CycleMaze(direction);
// score AI output
double score = 0.0;
//score = ScoreAI(outcome, game.traveler);
network.ScoreCycle(aiResult.CycleOutput.CycleID, score);
if (timerTimeout.HasValue)
{
Task.Delay(timerTimeout.Value).Wait();
}
if (perMoveDisplay)
{
MazeCycleComplete?.Invoke(game.traveler.location.X, game.traveler.location.Y, outcome.BumpedIntoWall);
}
if (!learn)
{
recentMoves.Add(new MoveDetails()
{
Direction = direction, MoveNumber = movesCnt
});
}
movesCnt++;
}
//tmr.Stop();
// compute final score
outcome.FinalScore = game.CalculateFinalScore(game.Moves);
// End AI Training
network.SessionEnd(outcome.FinalScore);
SessionComplete?.Invoke(network.SessionCount, outcome.FinalScore, movesCnt - 1);
if (movesCnt > HighestMoveCount)
{
HighestMoveCount = movesCnt;
}
return(outcome);
}