public Brain(int numInputs, int numActions)
{
CreatedDate = DateTime.Now;
TrainingTime = new TimeSpan();
NumInputs = numInputs;
NumActions = numActions;
// network
var layer1N = (numInputs + numActions) / 2;
Net = new Net();
Net.AddLayer(new InputLayer(1, 1, numInputs));
Net.AddLayer(new FullyConnLayer(layer1N));
Net.AddLayer(new ReluLayer());
Net.AddLayer(new FullyConnLayer(numActions));
Net.AddLayer(new RegressionLayer());
World = GridWorld.StandardState();
}
public void Train(int numGames, float initialRandomChance)
{
var gamma = 0.9f;
_trainer = new SgdTrainer(Net)
{
LearningRate = 0.01, Momentum = 0.0, BatchSize = 1, L2Decay = 0.001
};
var startTime = DateTime.Now;
for (var i = 0; i < numGames; i++)
{
World = GridWorld.StandardState();
double updatedReward;
var gameRunning = true;
var gameMoves = 0;
while (gameRunning)
{
//# We are in state S
//# Let's run our Q function on S to get Q values for all possible actions
var state = GetInputs();
var qVal = Net.Forward(state);
var action = 0;
if (Util.Rnd.NextDouble() < initialRandomChance)
{
//# Choose random action
action = Util.Rnd.Next(NumActions);
}
else
{
//# Choose best action from Q(s,a) values
action = MaxValueIndex(qVal);
}
//# Take action, observe new state S'
World.MovePlayer(action);
gameMoves++;
TotalTrainingMoves++;
var newState = GetInputs();
//# Observe reward
var reward = World.GetReward();
gameRunning = !World.GameOver();
//# Get max_Q(S',a)
var newQ = Net.Forward(newState);
var y = GetValues(newQ);
var maxQ = MaxValue(newQ);
if (gameRunning)
{
//# Non-terminal state
updatedReward = (reward + (gamma * maxQ));
}
else
{
//# Terminal state
updatedReward = reward;
TotalTrainingGames++;
Console.WriteLine($"Game: {TotalTrainingGames}. Moves: {gameMoves}. {(reward == 10 ? "WIN!" : "")}");
}
//# Target output
y[action] = updatedReward;
//# Feedback what the score would be for this action
_trainer.Train(state, y);
TotalLoss += _trainer.Loss;
}
//# Slowly reduce the chance of choosing a random action
if (initialRandomChance > 0.05f)
{
initialRandomChance -= (1f / numGames);
}
}
var duration = (DateTime.Now - startTime);
LastLoss = _trainer.Loss;
TrainingTime += duration;
Console.WriteLine($"Avg loss: {TotalLoss / TotalTrainingMoves}. Last: {LastLoss}");
Console.WriteLine($"Training duration: {duration}. Total: {TrainingTime}");
}
