private void BackgroundThread()
{
while (!needToStop)
{
if (!paused)
{
qAgent.tick();
UpdateUI(canvas);
}
Thread.Sleep(interval);
}
}
private void BackgroundThread()
{
while (!needToStop)
{
if (!paused)
{
// pause when all reward items have been found
if (txtReward.Text != "" && txtPunish.Text != "" && qAgent.tick(Double.Parse(txtReward.Text), Double.Parse(txtPunish.Text)) == 1)
{
paused = true;
}
UpdateUI(canvas);
}
Thread.Sleep(interval);
}
}
static void Main(string[] args) // b r o k e n
{
Console.ForegroundColor = ConsoleColor.DarkMagenta;
if (File.Exists(qAgentBrainPath))
{
using (FileStream fstream = new FileStream(qAgentBrainPath, FileMode.Open, FileAccess.Read, FileShare.Read)) {
qAgent = new BinaryFormatter().Deserialize(fstream) as QAgent;
qAgent.Reinitialize();
}
Console.WriteLine("QAgent loaded");
}
else
{
var num_inputs = 6; // 9 eyes, each sees 3 numbers (wall, green, red thing proximity)
var num_actions = 3; // 5 possible angles agent can turn
var temporal_window = 1; // amount of temporal memory. 0 = agent lives in-the-moment :)
var network_size = num_inputs * temporal_window + num_actions * temporal_window + num_inputs;
// config brain
var layer_defs = new List <LayerDefinition>();
// the value function network computes a value of taking any of the possible actions
// given an input state. Here we specify one explicitly the hard way
// but user could also equivalently instead use opt.hidden_layer_sizes = [20,20]
// to just insert simple relu hidden layers.
layer_defs.Add(new LayerDefinition {
type = "input", out_sx = 1, out_sy = 1, out_depth = network_size
});
layer_defs.Add(new LayerDefinition {
type = "fc", num_neurons = 96, activation = "relu"
});
layer_defs.Add(new LayerDefinition {
type = "fc", num_neurons = 96, activation = "relu"
});
layer_defs.Add(new LayerDefinition {
type = "fc", num_neurons = 96, activation = "relu"
});
layer_defs.Add(new LayerDefinition {
type = "regression", num_neurons = num_actions
});
// options for the Temporal Difference learner that trains the above net
// by backpropping the temporal difference learning rule.
//var opt = new Options { method="sgd", learning_rate=0.01, l2_decay=0.001, momentum=0.9, batch_size=10, l1_decay=0.001 };
Options opt = new Options {
method = "adadelta", l2_decay = 0.001, batch_size = 10
};
TrainingOptions tdtrainer_options = new TrainingOptions();
tdtrainer_options.temporal_window = temporal_window;
tdtrainer_options.experience_size = 30000;
tdtrainer_options.start_learn_threshold = 1000;
tdtrainer_options.gamma = 0.7;
tdtrainer_options.learning_steps_total = 200000;
tdtrainer_options.learning_steps_burnin = 3000;
tdtrainer_options.epsilon_min = 0.05;
tdtrainer_options.epsilon_test_time = 0.00;
tdtrainer_options.layer_defs = layer_defs;
tdtrainer_options.options = opt;
DeepQLearn brain = new DeepQLearn(num_inputs, num_actions, tdtrainer_options);
qAgent = new QAgent(brain);
}
qAgent.startlearn();
new Thread(() => {
while (true)
{
if (DateTime.Now.Ticks / TimeSpan.TicksPerMillisecond % 31 /*arbitrary*/ == 0)
{
using (FileStream fstream = new FileStream(qAgentBrainPath, FileMode.Create, FileAccess.Write, FileShare.ReadWrite)) {
new BinaryFormatter().Serialize(fstream, qAgent);
}
}
qAgent.tick();
}
}).Start();
}