private void UpdateTargetNet()
{
targetNet = new SequentialNet(policyNet.Layers);
Layer[] layers = policyNet.Layers.ToArray();
for (int i = 0; i < layers.Length; i++)
{
layers[i] = layers[i].Copy();
layers[i].IsInputLayer = i == 0;
layers[i].LastLayer = i > 0 ? layers[i - 1] : null;
}
targetNet.Layers = layers;
}
/// <summary>
/// Start
/// </summary>
private void Start()
{
nn = nnBehaviour.GetSequentialNet();
nn.Init(true, 1f);
nn.PrintModelInfo(true);
Trainer trainer = new Trainers.BackPropagation(nn, Errors.MeanSquaredError, 0.05f);
float[][] inputs = new float[dataTemplate.Length][];
float[][] expectedOutputs = new float[dataTemplate.Length][];
for (int i = 0; i < inputs.Length; i++)
{
float[][] sample = dataTemplate[i];
inputs[i] = sample[0];
expectedOutputs[i] = sample[1];
}
trainer.Train(inputs, expectedOutputs, 400);
}
private void Awake()
{
if (env == null)
{
throw new Exception("Environment is null!");
}
if (ActionsSize < 2)
{
throw new Exception("Agent must have 2 or more actions");
}
policyNet = GetComponent <SequentialNetBehaviour>().GetSequentialNet();
policyNet.Init();
UpdateTargetNet();
policyNetTrainer = new Trainers.BackPropagation(policyNet, Errors.MeanSquaredError, LearningRate, false);
replayMemory = new ReplayMemory(replayMemoryCapacity, memorySampleSize);
explorationRate = MaxExplorationRate;
}
