Method sample_action

rl3/es_flappy.py:98–105

(self, x)

    return softmax(Z.dot(self.W2) + self.b2)

  def sample_action(self, x):
    # assume input is a single state of size (D,)
    # first make it (N, D) to fit ML conventions
    X = np.atleast_2d(x)
    P = self.forward(X)
    p = P[0] # the first row
    # return np.random.choice(len(p), p=p)
    return np.argmax(p)

  def get_params(self):
    # return a flat array of parameters