diff --git a/baselines/common/running_mean_std.py b/baselines/common/running_mean_std.py
index 28d0ba1..9573cb0 100644
--- a/baselines/common/running_mean_std.py
+++ b/baselines/common/running_mean_std.py
@@ -41,15 +41,15 @@ class TfRunningMeanStd(object):
     def __init__(self, epsilon=1e-4, shape=(), scope=''):
         sess = get_session()
 
-        _batch_mean = tf.placeholder(shape=shape, dtype=tf.float64)
-        _batch_var = tf.placeholder(shape=shape, dtype=tf.float64)
-        _batch_count = tf.placeholder(shape=(), dtype=tf.float64)
+        _batch_mean = tf.placeholder(shape=shape, dtype=tf.float32)
+        _batch_var = tf.placeholder(shape=shape, dtype=tf.float32)
+        _batch_count = tf.placeholder(shape=(), dtype=tf.float32)
 
         
         with tf.variable_scope(scope, reuse=tf.AUTO_REUSE):
-            _mean  = tf.get_variable('mean',  initializer=np.zeros(shape, 'float64'),     dtype=tf.float64)
-            _var   = tf.get_variable('std',   initializer=np.ones(shape, 'float64'),      dtype=tf.float64)    
-            _count = tf.get_variable('count', initializer=np.ones((), 'float64')*epsilon, dtype=tf.float64)
+            _mean  = tf.get_variable('mean',  initializer=np.zeros(shape, 'float32'),      dtype=tf.float32)
+            _var   = tf.get_variable('std',   initializer=np.ones(shape, 'float32'),       dtype=tf.float32)    
+            _count = tf.get_variable('count', initializer=np.full((), epsilon, 'float32'), dtype=tf.float32)
 
         delta = _batch_mean - _mean
         tot_count = _count + _batch_count
@@ -78,35 +78,29 @@ class TfRunningMeanStd(object):
     
         def update_from_moments(batch_mean, batch_var, batch_count):               
             for op in update_ops:
-                sess.run(update_ops, feed_dict={
+                sess.run(op, feed_dict={
                     _batch_mean: batch_mean, 
                     _batch_var: batch_var,
                     _batch_count: batch_count
                 })
+        
                 
 
         sess.run(tf.variables_initializer([_mean, _var, _count]))
         self.sess = sess
         self.update_from_moments = update_from_moments
+        self._set_mean_var_count()
 
-    @property
-    def mean(self):
-        return self.sess.run(self._mean)
-
-    @property
-    def var(self):
-        return self.sess.run(self._var)
-
-    @property
-    def count(self):
-        return self.sess.run(self._count)
-
+    def _set_mean_var_count(self):
+        self.mean, self.var, self.count = self.sess.run([self._mean, self._var, self._count])
          
     def update(self, x):
         batch_mean = np.mean(x, axis=0)
         batch_var = np.var(x, axis=0)
         batch_count = x.shape[0]
         self.update_from_moments(batch_mean, batch_var, batch_count)
+        self._set_mean_var_count()
+        
 
           
 
diff --git a/baselines/common/vec_env/vec_normalize.py b/baselines/common/vec_env/vec_normalize.py
index 37ee02f..2f2cc8d 100644
--- a/baselines/common/vec_env/vec_normalize.py
+++ b/baselines/common/vec_env/vec_normalize.py
@@ -10,8 +10,8 @@ class VecNormalize(VecEnvWrapper):
         VecEnvWrapper.__init__(self, venv)
         self.ob_rms = RunningMeanStd(shape=self.observation_space.shape) if ob else None
         self.ret_rms = RunningMeanStd(shape=()) if ret else None
-        #self.ob_rms = TfRunningMeanStd(shape=self.observation_space.shape, scope='observation_running_mean_std') if ob else None
-        #self.ret_rms = TfRunningMeanStd(shape=(), scope='return_running_mean_std') if ret else None
+        self.ob_rms = TfRunningMeanStd(shape=self.observation_space.shape, scope='observation_running_mean_std') if ob else None
+        self.ret_rms = TfRunningMeanStd(shape=(), scope='return_running_mean_std') if ret else None
         self.clipob = clipob
         self.cliprew = cliprew
         self.ret = np.zeros(self.num_envs)
diff --git a/baselines/run.py b/baselines/run.py
index 344f035..cba8515 100644
--- a/baselines/run.py
+++ b/baselines/run.py
@@ -4,10 +4,11 @@ import os
 import os.path as osp
 import gym
 from collections import defaultdict
+import tensorflow as tf
 
 from baselines.common.vec_env.vec_frame_stack import VecFrameStack
 from baselines.common.cmd_util import common_arg_parser, parse_unknown_args, make_mujoco_env, make_atari_env
-from baselines.common.tf_util import save_state, load_state
+from baselines.common.tf_util import save_state, load_state, get_session
 from baselines import bench, logger
 from importlib import import_module
 
@@ -84,6 +85,10 @@ def build_env(args, render=False):
 
     env_type, env_id = get_env_type(args.env)
     if env_type == 'mujoco':
+        get_session(tf.ConfigProto(allow_soft_placement=True,
+                                   intra_op_parallelism_threads=1, 
+                                   inter_op_parallelism_threads=1))
+
         if args.num_env:
             env = SubprocVecEnv([lambda: make_mujoco_env(env_id, seed + i if seed is not None else None, args.reward_scale) for i in range(args.num_env)])    
         else:
@@ -193,6 +198,7 @@ def main():
     args, unknown_args = arg_parser.parse_known_args()
     extra_args = {k: parse(v) for k,v in parse_unknown_args(unknown_args).items()}
 
+    
     if MPI is None or MPI.COMM_WORLD.Get_rank() == 0:
         rank = 0
         logger.configure()