change atari preprocessing to use faster opencv

some logger changes

baselines/ddpg/main.py

@@ -19,11 +19,12 @@ from mpi4py import MPI
 def run(env_id, seed, noise_type, layer_norm, evaluation, **kwargs):
     # Configure things.
     rank = MPI.COMM_WORLD.Get_rank()
-    if rank != 0: logger.set_level(logger.DISABLED)
+    if rank != 0:
+        logger.set_level(logger.DISABLED)
 
     # Create envs.
     env = gym.make(env_id)
-    env = bench.Monitor(env, logger.get_dir() and os.path.join(logger.get_dir(), "%i.monitor.json"%rank))
+    env = bench.Monitor(env, logger.get_dir() and os.path.join(logger.get_dir(), str(rank)))
     gym.logger.setLevel(logging.WARN)
 
     if evaluation and rank==0:
@@ -81,7 +82,7 @@ def run(env_id, seed, noise_type, layer_norm, evaluation, **kwargs):
 
 def parse_args():
     parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-    
+
     parser.add_argument('--env-id', type=str, default='HalfCheetah-v1')
     boolean_flag(parser, 'render-eval', default=False)
     boolean_flag(parser, 'layer-norm', default=True)
@@ -103,11 +104,21 @@ def parse_args():
     parser.add_argument('--nb-eval-steps', type=int, default=100)  # per epoch cycle and MPI worker
     parser.add_argument('--nb-rollout-steps', type=int, default=100)  # per epoch cycle and MPI worker
     parser.add_argument('--noise-type', type=str, default='adaptive-param_0.2')  # choices are adaptive-param_xx, ou_xx, normal_xx, none
+    parser.add_argument('--num-timesteps', type=int, default=None)
     boolean_flag(parser, 'evaluation', default=False)
-    return vars(parser.parse_args())
+    args = parser.parse_args()
+    # we don't directly specify timesteps for this script, so make sure that if we do specify them
+    # they agree with the other parameters
+    if args.num_timesteps is not None:
+        assert(args.num_timesteps == args.nb_epochs * args.nb_epoch_cycles * args.nb_rollout_steps)
+    dict_args = vars(args)
+    del dict_args['num_timesteps']
+    return dict_args
 
 
 if __name__ == '__main__':
     args = parse_args()
+    if MPI.COMM_WORLD.Get_rank() == 0:
+        logger.configure()
     # Run actual script.
     run(**args)

baselines/ddpg/util.py

@@ -1,6 +1,3 @@
-import time
-
-import gym
 import numpy as np
 import tensorflow as tf
 from mpi4py import MPI