old_contents: #! /usr/bin/env python
  """
  Execute a training run of deep-Q-Leaning with parameters that
  are consistent with:
  
  Human-level control through deep reinforcement learning.
  Nature, 518(7540):529-533, February 2015
  
  """
  
  import launcher
  import sys
  
  class Defaults:
      # ----------------------
      # RLGlue Parameters
      # ----------------------
      RLGLUE_PORT = 4096
  
      # ----------------------
      # Experiment Parameters
      # ----------------------
      STEPS_PER_EPOCH = 250000
      EPOCHS = 200
      STEPS_PER_TEST = 125000
  
      # ----------------------
      # ALE Parameters
      # ----------------------
      BASE_ROM_PATH = "../roms/"
      ROM = 'breakout.bin'
      FRAME_SKIP = 4
  
      # ----------------------
      # Agent/Network parameters:
      # ----------------------
      LEARNING_RATE = .00025
      DISCOUNT = .99
      RMS_DECAY = .95 # (Rho)
      RMS_EPSILON = .01 
      MOMENTUM = 0 # Note that the "momentum" value mentioned in the Nature
                   # paper is not used in the same way as a traditional momentum
                   # term.  It is used to track gradient for the purpose of 
                   # estimating the standard deviation. This package uses
                   # rho/RMS_DECAY to track both the history of the gradient
                   # and the squared gradient. 
      EPSILON_START = 1.0
      EPSILON_MIN = .1
      EPSILON_DECAY = 1000000
      PHI_LENGTH = 4
      UPDATE_FREQUENCY = 4
      REPLAY_MEMORY_SIZE = 1000000
      BATCH_SIZE = 32
      NETWORK_TYPE = "nature_dnn"
      FREEZE_INTERVAL = 10000
      REPLAY_START_SIZE = 50000
      IMAGE_RESIZE = 'scale'
  
  if __name__ == "__main__":
      launcher.launch(sys.argv[1:], Defaults, __doc__)
  
new_contents: #! /usr/bin/env python
  """
  Execute a training run of deep-Q-Leaning with parameters that
  are consistent with:
  
  Human-level control through deep reinforcement learning.
  Nature, 518(7540):529-533, February 2015
  
  """
  
  import launcher
  import sys
  
  class Defaults:
      # ----------------------
      # RLGlue Parameters
      # ----------------------
      RLGLUE_PORT = 4096
  
      # ----------------------
      # Experiment Parameters
      # ----------------------
      STEPS_PER_EPOCH = 250000
      EPOCHS = 200
      STEPS_PER_TEST = 125000
  
      # ----------------------
      # ALE Parameters
      # ----------------------
      BASE_ROM_PATH = "../roms/"
      ROM = 'breakout.bin'
      FRAME_SKIP = 4
  
      # ----------------------
      # Agent/Network parameters:
      # ----------------------
      LEARNING_RATE = .00025
      DISCOUNT = .99
      RMS_DECAY = .95 # (Rho)
      RMS_EPSILON = .01
      MOMENTUM = 0 # Note that the "momentum" value mentioned in the Nature
                   # paper is not used in the same way as a traditional momentum
                   # term.  It is used to track gradient for the purpose of
                   # estimating the standard deviation. This package uses
                   # rho/RMS_DECAY to track both the history of the gradient
                   # and the squared gradient.
      EPSILON_START = 1.0
      EPSILON_MIN = .1
      EPSILON_DECAY = 1000000
      PHI_LENGTH = 4
      UPDATE_FREQUENCY = 4
      REPLAY_MEMORY_SIZE = 1000000
      BATCH_SIZE = 32
      NETWORK_TYPE = "nature_dnn"
      FREEZE_INTERVAL = 10000
      REPLAY_START_SIZE = 50000
      IMAGE_RESIZE = 'scale'
  
  if __name__ == "__main__":
      launcher.launch(sys.argv[1:], Defaults, __doc__)
  
subject: Tidy up white-space