old_contents: import tensorflow as tf
  
  flags = tf.app.flags
  FLAGS = flags.FLAGS
  
  flags.DEFINE_integer('warmup', 1000, 'time without training but only filling the replay memory')
  flags.DEFINE_integer('bsize', 256, 'minibatch size')
  flags.DEFINE_integer('iter', 1, 'train iters each timestep')
  flags.DEFINE_integer('l1size', 200, '1st layer size')
  flags.DEFINE_integer('l2size', 200, '2nd layer size')
  flags.DEFINE_integer('rmsize', 500000, 'memory size')
  
  flags.DEFINE_float('tau', 0.01, 'moving average for target network')
  flags.DEFINE_float('discount', 0.99, '')
  flags.DEFINE_float('l2norm', 0.0001, 'l2 weight decay')
  flags.DEFINE_float('pl2norm', 0., 'policy network l2 weight decay (only for DDPG)')
  flags.DEFINE_float('rate', 0.001, 'learning rate')
  flags.DEFINE_float('prate', 0.0001, 'policy net learning rate (only for DDPG)')
  flags.DEFINE_float('outheta', 0.15, 'noise theta')
  flags.DEFINE_float('ousigma', 0.1, 'noise sigma')
  flags.DEFINE_float('lrelu', 0.01, 'leak relu rate')
  flags.DEFINE_boolean('naf_bn', False, 'enable NAF batch normalization')
  flags.DEFINE_boolean('icnn_bn', False, 'enable icnn batch normalization')
  
  flags.DEFINE_string('icnn_opt', 'adam',
                      "ICNN's inner optimization routine. Options=[adam,bundle_entropy]")
  
  flags.DEFINE_integer('thread', 1, 'tensorflow threads')
  
  flags.DEFINE_boolean('summary', True, 'use tensorboard log')
  
  flags.DEFINE_float('initstd', 0.01, 'weight init std (DDPG uses its own initialization)')
  
new_contents: import tensorflow as tf
  
  flags = tf.app.flags
  FLAGS = flags.FLAGS
  
  flags.DEFINE_integer('warmup', 1000, 'time without training but only filling the replay memory')
  flags.DEFINE_integer('bsize', 256, 'minibatch size')
  flags.DEFINE_integer('iter', 1, 'train iters each timestep')
  flags.DEFINE_integer('l1size', 200, '1st layer size')
  flags.DEFINE_integer('l2size', 200, '2nd layer size')
  flags.DEFINE_integer('rmsize', 500000, 'memory size')
  
  flags.DEFINE_float('tau', 0.01, 'moving average for target network')
  flags.DEFINE_float('discount', 0.99, '')
  flags.DEFINE_float('l2norm', 0.0001, 'l2 weight decay')
  flags.DEFINE_float('pl2norm', 0., 'policy network l2 weight decay (only for DDPG)')
  flags.DEFINE_float('rate', 0.001, 'learning rate')
  flags.DEFINE_float('prate', 0.0001, 'policy net learning rate (only for DDPG)')
  flags.DEFINE_float('outheta', 0.15, 'noise theta')
  flags.DEFINE_float('ousigma', 0.1, 'noise sigma')
  flags.DEFINE_float('lrelu', 0.01, 'leak relu rate')
  flags.DEFINE_boolean('naf_bn', False, 'enable NAF batch normalization')
  flags.DEFINE_boolean('icnn_bn', False, 'enable icnn batch normalization')
  
  flags.DEFINE_string('icnn_opt', 'adam',
                      "ICNN's inner optimization routine. Options=[adam,bundle_entropy]")
  
  flags.DEFINE_integer('thread', 1, 'tensorflow threads')
  
  flags.DEFINE_boolean('summary', False, 'use tensorboard log')
  
  flags.DEFINE_float('initstd', 0.01, 'weight init std (DDPG uses its own initialization)')
  
subject: Disable tensorboard log by default.