(self, width, height, rng, max_steps=1000, phi_length=4)
Construct a DataSet.

    Arguments:
      width, height - image size
      max_steps - the number of time steps to store
      phi_length - number of images to concatenate into a state
      rng - initialized numpy random number generator, used to
      choose random minibatches
Construct a DataSet.
Construct a DataSet .
def __init__(self, width, height, rng, max_steps=1000, phi_length=4):
    """Construct a DataSet.

    Arguments:
      width, height - image size
      max_steps - the number of time steps to store
      phi_length - number of images to concatenate into a state
      rng - initialized numpy random number generator, used to
      choose random minibatches

    """
    # TODO: Specify capacity in number of state transitions, not
    # number of saved time steps.

    # Store arguments.
    self.width = width
    self.height = height
    self.max_steps = max_steps
    self.phi_length = phi_length
    self.rng = rng

    # Allocate the circular buffers and indices.
    self.imgs = np.zeros((max_steps, height, width), dtype='uint8')
    self.actions = np.zeros(max_steps, dtype='int32')
    self.rewards = np.zeros(max_steps, dtype=floatX)
    self.terminal = np.zeros(max_steps, dtype='bool')
    
    self.bottom = 0
    self.top = 0
    self.size = 0
def __init__ ( self , width , height , rng , max_steps = 1000 , phi_length = 4 ) : # TODO: Specify capacity in number of state transitions, not # number of saved time steps. # Store arguments. self . width = width self . height = height self . max_steps = max_steps self . phi_length = phi_length self . rng = rng # Allocate the circular buffers and indices. self . imgs = np . zeros ( ( max_steps , height , width ) , dtype = 'uint8' ) self . actions = np . zeros ( max_steps , dtype = 'int32' ) self . rewards = np . zeros ( max_steps , dtype = floatX ) self . terminal = np . zeros ( max_steps , dtype = 'bool' ) self . bottom = 0 self . top = 0 self . size = 0