public void runTest(MarkovGridGame game, int seed)
{
int inputSize = game.width * game.height * 4;
rand = new Random(seed);
NeuralNet net = new NeuralNet(new int[] { inputSize, inputSize / 2, 8, game.numberOfActions });
// Initialize outside of loops
QNetState currentState;
ReplayMemory replay;
int numActions;
// Loop over total number of games
for (int epoch = 0; epoch < maxEpochs; epoch++)
{
replay = new ReplayMemory();
numActions = 0;
// Loop until current game is completed or maximum number of steps
while (!game.isCompleted && numActions < maxActions)
{
currentState = game.toQNetState();
Console.Clear();
Console.WriteLine(game);
// With probability epsilon select a random action aT
if (rand.NextDouble() <= epsilon.getE())
{
// Select a random action
// Explore instead of Exploit
currentState.chosenAction = rand.Next(0, game.numberOfActions);
}
else
{
// Exploit instead of Explore
// Convert to input
double[] networkInput = new double[currentState.input.Count()];
for (int i = 0; i < currentState.input.Count(); i++)
{
networkInput[i] = currentState.input[i];
}
net.setInput(networkInput);
net.forwardPass();
double[] output = net.Output;
for (int i = 0; i < output.Count(); i++)
{
Console.WriteLine(output[i]);
}
// Ugly inefficient implementation
currentState.chosenAction = output.ToList().IndexOf(output.Max());
net.backPropogate(.05, output);
}
Console.WriteLine(numActions);
game.acceptAction(currentState.chosenAction);
numActions++;
replay.Add(currentState);
}
memoryBank.Add(replay);
game.reset();
}
//BackPropogationRunner bpr = new BackPropogationRunner();
}