diff --git a/baselines/gail/trpo_mpi.py b/baselines/gail/trpo_mpi.py
index 57c28bdb0a..6b973756bd 100644
--- a/baselines/gail/trpo_mpi.py
+++ b/baselines/gail/trpo_mpi.py
@@ -146,8 +146,9 @@ def learn(env, policy_func, reward_giver, expert_dataset, rank,
     dist = meankl
 
     all_var_list = pi.get_trainable_variables()
-    var_list = [v for v in all_var_list if v.name.split("/")[1].startswith("pol")]
-    vf_var_list = [v for v in all_var_list if v.name.split("/")[1].startswith("vf")]
+    var_list = [v for v in all_var_list if v.name.split("/")[1] == "pol"]
+    vf_var_list = [v for v in all_var_list if v.name.split("/")[1] == "vf"]
+    assert len(var_list) == len(vf_var_list) + 1
     d_adam = MpiAdam(reward_giver.get_trainable_variables())
     vfadam = MpiAdam(vf_var_list)
 
diff --git a/baselines/ppo1/mlp_policy.py b/baselines/ppo1/mlp_policy.py
index e03284a03b..7f979b3495 100644
--- a/baselines/ppo1/mlp_policy.py
+++ b/baselines/ppo1/mlp_policy.py
@@ -22,21 +22,23 @@ def _init(self, ob_space, ac_space, hid_size, num_hid_layers, gaussian_fixed_var
         with tf.variable_scope("obfilter"):
             self.ob_rms = RunningMeanStd(shape=ob_space.shape)
 
-        obz = tf.clip_by_value((ob - self.ob_rms.mean) / self.ob_rms.std, -5.0, 5.0)
-        last_out = obz
-        for i in range(num_hid_layers):
-            last_out = tf.nn.tanh(tf.layers.dense(last_out, hid_size, name="vffc%i"%(i+1), kernel_initializer=U.normc_initializer(1.0)))
-        self.vpred = tf.layers.dense(last_out, 1, name='vffinal', kernel_initializer=U.normc_initializer(1.0))[:,0]
-
-        last_out = obz
-        for i in range(num_hid_layers):
-            last_out = tf.nn.tanh(tf.layers.dense(last_out, hid_size, name='polfc%i'%(i+1), kernel_initializer=U.normc_initializer(1.0)))
-        if gaussian_fixed_var and isinstance(ac_space, gym.spaces.Box):
-            mean = tf.layers.dense(last_out, pdtype.param_shape()[0]//2, name='polfinal', kernel_initializer=U.normc_initializer(0.01))
-            logstd = tf.get_variable(name="logstd", shape=[1, pdtype.param_shape()[0]//2], initializer=tf.zeros_initializer())
-            pdparam = tf.concat([mean, mean * 0.0 + logstd], axis=1)
-        else:
-            pdparam = tf.layers.dense(last_out, pdtype.param_shape()[0], name='polfinal', kernel_initializer=U.normc_initializer(0.01))
+        with tf.variable_scope('vf'):
+            obz = tf.clip_by_value((ob - self.ob_rms.mean) / self.ob_rms.std, -5.0, 5.0)
+            last_out = obz
+            for i in range(num_hid_layers):
+                last_out = tf.nn.tanh(tf.layers.dense(last_out, hid_size, name="fc%i"%(i+1), kernel_initializer=U.normc_initializer(1.0)))
+            self.vpred = tf.layers.dense(last_out, 1, name='final', kernel_initializer=U.normc_initializer(1.0))[:,0]
+
+        with tf.variable_scope('pol'):
+            last_out = obz
+            for i in range(num_hid_layers):
+                last_out = tf.nn.tanh(tf.layers.dense(last_out, hid_size, name='fc%i'%(i+1), kernel_initializer=U.normc_initializer(1.0)))
+            if gaussian_fixed_var and isinstance(ac_space, gym.spaces.Box):
+                mean = tf.layers.dense(last_out, pdtype.param_shape()[0]//2, name='final', kernel_initializer=U.normc_initializer(0.01))
+                logstd = tf.get_variable(name="logstd", shape=[1, pdtype.param_shape()[0]//2], initializer=tf.zeros_initializer())
+                pdparam = tf.concat([mean, mean * 0.0 + logstd], axis=1)
+            else:
+                pdparam = tf.layers.dense(last_out, pdtype.param_shape()[0], name='final', kernel_initializer=U.normc_initializer(0.01))
 
         self.pd = pdtype.pdfromflat(pdparam)