Method __init__

rl2/mountaincar/pg_theano.py:153–197

(self, D, ft, hidden_layer_sizes=[])

# approximates V(s)
class ValueModel:
  def __init__(self, D, ft, hidden_layer_sizes=[]):
    self.ft = ft

    # create the graph
    self.layers = []
    M1 = D
    for M2 in hidden_layer_sizes:
      layer = HiddenLayer(M1, M2)
      self.layers.append(layer)
      M1 = M2

    # final layer
    layer = HiddenLayer(M1, 1, lambda x: x)
    self.layers.append(layer)

    # get all params for gradient later
    params = []
    for layer in self.layers:
      params += layer.params

    # inputs and targets
    X = T.matrix('X')
    Y = T.vector('Y')

    # calculate output and cost
    Z = X
    for layer in self.layers:
      Z = layer.forward(Z)
    Y_hat = T.flatten(Z)
    cost = T.sum((Y - Y_hat)**2)

    # specify update rule
    updates = adam(cost, params, lr0=1e-1)

    # compile functions
    self.train_op = theano.function(
      inputs=[X, Y],
      updates=updates,
      allow_input_downcast=True
    )
    self.predict_op = theano.function(
      inputs=[X],
      outputs=Y_hat,
      allow_input_downcast=True
    )

  def partial_fit(self, X, Y):
    X = np.atleast_2d(X)