public void FillRabbits()
{
Debug("fill rabbits");
int cnt = Mia.Rabbits.rabbits.Count;
int i = 0;
_cmd.CommandText = "ALTER TABLE `rabbits` DISABLE KEYS;";
_cmd.ExecuteNonQuery();
foreach (Rabbit r in Mia.Rabbits.rabbits)
{
i++;
Mia.Setpb(i, cnt);
this.FillRabbit(r, 0, false);
}
_cmd.CommandText = "ALTER TABLE `rabbits` ENABLE KEYS;";
_cmd.ExecuteNonQuery();
Debug("normalizing fuckers");
NormalizeFuckers();
}
static void BackPropTraining()
{
var mia = new Mia();
Nou nou = (Nou) new NouFactory().CreateInstance();
mia.IsAiHost = true;
nou.IsAiHost = true;
var p1 = new PlayerState(1);
var p2 = new PlayerState(2);
var p1Sync = new PlayerState.Sync {
PlayerNumber = 1
};
var p2Sync = new PlayerState.Sync {
PlayerNumber = 2
};
// Generate random Board states and let Nou learn what Mia would do.
var rnd = new Random();
Console.Write("Training....");
var left = Console.CursorLeft;
var top = Console.CursorTop;
for (int i = 0; i < 10000; ++i)
{
Console.SetCursorPosition(left, top);
Console.Write(i + 1);
var sync = new GameSync
{
Player1 = p1Sync,
Player2 = p2Sync
};
sync.Phase = GamePhase.Player1Turn;
sync.FieldsWithCards = new List <BoardField.Sync>();
// Random Board
// 8 Cards on each side, 4x Virus, 4x Link
List <OnlineCard> cards = new List <OnlineCard>();
for (int j = 0; j < 4; ++j)
{
cards.Add(new OnlineCard {
Owner = p1, Type = OnlineCardType.Link
});
cards.Add(new OnlineCard {
Owner = p2, Type = OnlineCardType.Link
});
cards.Add(new OnlineCard {
Owner = p1, Type = OnlineCardType.Virus
});
cards.Add(new OnlineCard {
Owner = p2, Type = OnlineCardType.Virus
});
}
var board = new List <BoardField>();
for (ushort y = 0; y < 11; ++y)
{
for (ushort x = 0; x < 8; ++x)
{
board.Add(new BoardField(x, y));
}
}
while (cards.Count > 0)
{
// 90% propability of beeing on board ( y <= 7 )
int ymax = rnd.NextDouble() <= .9 ? 7 : 9;
int boardIndex = rnd.Next(board.Count);
if (board[boardIndex].Y > ymax)
{
continue;
}
var field = board[boardIndex];
board.RemoveAt(boardIndex);
field.Card = cards[0];
cards.RemoveAt(0);
sync.FieldsWithCards.Add(field.GetSync());
}
mia.Synchronize(sync);
nou.Train(sync, mia.PlayTurn());
}
Console.WriteLine("\nFinished. Press any key to continue");
Console.ReadKey();
}
static void Main(string[] args)
{
Console.WriteLine("NOU AI Trainer\n");
Console.WriteLine("Select mode: ");
Console.WriteLine("\t1: Genetic algorithm against MIA AI. (default)");
Console.WriteLine("\t2: Backproagation learning from MIA AI.");
var usrInput = Console.ReadLine();
if (usrInput == "2")
{
BackPropTraining();
return;
}
Console.Clear();
Console.WriteLine("NOU AI Trainer\n");
string dir = "Nou_AI";
string genTxt = dir + "\\gen.txt";
string genLogTxt = dir + "\\genLog.txt";
Func <int, int, string> netFile = (a, b) => { return(dir + "\\net" + a + "." + b + ".txt"); };
if (!Directory.Exists(dir))
{
Directory.CreateDirectory(dir);
}
if (File.Exists(genTxt))
{
uint.TryParse(File.ReadAllText(genTxt), out gen);
}
Console.WriteLine("Current generation: " + gen);
Console.WriteLine("Please enter number of iterations: ");
var inp = Console.ReadLine();
if (!uint.TryParse(inp, out uint gen2Go))
{
Console.WriteLine("Not a number! Press any key to exit");
Console.ReadKey();
return;
}
Console.CursorVisible = false;
Trainer.MaxRound = 15;
// We have 50 AIs. All fight the same MIA instance.
int aiCnt = 50; // Number of AIs per generation
double mutationDelta = Nou.MutateDelta / 3;
var log = new Dictionary <int, TrainingLog>();
var fac = new NouFactory(); // Loads pretrained NouAi_0.txt
for (int i = 0; i < aiCnt; ++i)
{
log.Add(i, new TrainingLog {
AI = (Nou)fac.CreateInstance(), ID = i
});
}
// Check if there are saved nets on the hard drive
if (gen > 0) // Ignore if we start from gen 0
{
for (int i = 0; i < aiCnt; ++i)
{
log[i].AI.ReadFromFile(0, netFile(i, 0));
log[i].AI.ReadFromFile(1, netFile(i, 1));
}
}
var rnd = new Random(); // Used for applying seed to MIA.
genProgress = 0;
maxGen = gen + gen2Go;
byte mutateFlags = 1; // Only train net1.
// Cycle through all defined generations
for (int it = 0; it < gen2Go; ++it)
{
genProgress = (int)(0.5 + 100.0 * ((1.0 * it) / gen2Go));
int curGenSeed = rnd.Next();
int nouSeed = rnd.Next();
#region NextGen Prep
if (it > 0)
{
// Based on 50 AIs:
// 1. Delete the 25 least scoring nets
// 2. Keep the best 5 nets untouched // +5 -> 5
// 3. Mutate the remaining 20 nets // +20 -> 25
// 4. Create 4 copies of the 5 best
// nets and mutate them // +20 -> 45
// 5. Add 5 ranom nets // +5 -> 50
// We do not create new randoms AIs.
// All our progress might get lost if a random ai accidentally scores high
// 1. + 2.
// Sort by score
var sortedList = log.Select(o => o.Value).OrderByDescending(o => o.Score).ToList();
log.Clear(); // Clear list
for (int i = 0; i < aiCnt / 2; ++i) // Add the 25 best nets
{
sortedList[i].ID = i; // Update ID
log.Add(i, sortedList[i]);
}
// 3. Mutate the last 20 nets in the new list
for (int i = 5; i < aiCnt / 2; ++i)
{
log[i].AI.Mutate(mutationDelta, mutateFlags);
}
// 4.
int fillerRatio = (aiCnt / 2 - 5) / 4; // =5 when aiCnt=50
int nId = aiCnt / 2;
for (int iAi = 0; iAi < 5; iAi++) // The first five AIs
{
for (int ii = 0; ii < fillerRatio; ++ii)
{
var nou = Nou.Copy(log[iAi].AI);
nou.Mutate(mutationDelta, mutateFlags);
log[nId] = new TrainingLog()
{
ID = nId, AI = nou
};
++nId;
}
}
// 5.
while (log.Count < aiCnt)
{
log.Add(log.Count, new TrainingLog {
ID = log.Count, AI = (Nou)fac.CreateInstance()
});
}
}
// Reset score at the start of each generation
for (int ai = 0; ai < aiCnt; ++ai)
{
log[ai].Score = 0;
}
#endregion
Trainer trainer = null;
// Cycle through all AIs
Parallel.ForEach(log.Select(o => o.Value), new ParallelOptions()
{
MaxDegreeOfParallelism = 8
}, (aiLog) =>
{
Trainer trn = new Trainer();
var mia = new Mia();
mia.SetSeed(curGenSeed);
mia.Depth = 1;
if (aiLog.ID == 0)
{
trainer = trn;
trn.AiDelay = 0; // This is the displayed trainer.
}
var waitHandle = new AutoResetEvent(false);
EventHandler handler = (s, a) =>
{
if (trn.GameOver)
{
try { waitHandle.Set(); } catch { }
}
};
trn.NeedsUiUpdate += handler;
aiLog.AI.DeploySeed = nouSeed;
// Run game:
trn.StartGame(aiLog.AI, mia);
waitHandle.WaitOne(100 * Trainer.MaxRound); // Give 100ms per round
trn.NeedsUiUpdate -= handler;
trn.Abort = true;
if (trn.Game.Phase == GamePhase.Player1Win)
{
Interlocked.Increment(ref nouWins);
}
else if (trn.Game.Phase == GamePhase.Player2Win)
{
Interlocked.Increment(ref miaWins);
}
// Post Game: Add score
aiLog.Score = aiLog.AI.Fitness();
});
genProgress = (int)(0.5 + 100.0 * ((it + 1.0) / gen2Go));
if (trainer != null)
{
DrawBoard(trainer);
}
// Log score to file
++gen;
try
{
var genSorted = log.Select(o => o.Value).OrderByDescending(o => o.Score).ToList();
using (var f = File.AppendText(genLogTxt))
{
f.WriteLine("Gen:\t" + gen + "\tScore:\t" + genSorted[0].Score.ToString(System.Globalization.CultureInfo.InvariantCulture));
}
}
catch { }
}
// Save all current Nets
for (int i = 0; i < aiCnt; ++i)
{
log[i].AI.Net1.SaveAsFile(netFile(i, 0));
log[i].AI.Net2.SaveAsFile(netFile(i, 1));
}
File.WriteAllText(genTxt, gen.ToString());
System.Threading.Thread.Sleep(500); // Wait for UI updates
Console.CursorVisible = true;
Console.WriteLine("Finished. Press any key to exit");
Console.ReadKey();
}
