diff --git a/python/ray/rllib/models/action_dist.py b/python/ray/rllib/models/action_dist.py
index b104230bf..c3a816709 100644
--- a/python/ray/rllib/models/action_dist.py
+++ b/python/ray/rllib/models/action_dist.py
@@ -6,8 +6,6 @@ import tensorflow as tf
 import numpy as np
 import distutils.version
 
-from ray.rllib.utils.reshaper import Reshaper
-
 use_tf150_api = (distutils.version.LooseVersion(tf.VERSION) >=
                  distutils.version.LooseVersion("1.5.0"))
 
@@ -182,10 +180,10 @@ class MultiActionDistribution(ActionDistribution):
         inputs (Tensor list): A list of tensors from which to compute samples.
     """
 
-    def __init__(self, inputs, action_space, child_distributions):
-        # you actually have to instantiate the child distributions
-        self.reshaper = Reshaper(action_space.spaces)
-        split_inputs = self.reshaper.split_tensor(inputs)
+    def __init__(self, inputs, action_space, child_distributions, input_lens):
+        self.input_lens = input_lens
+        inputs = tf.reshape(inputs, [-1, sum(input_lens)])
+        split_inputs = tf.split(inputs, self.input_lens, axis=1)
         child_list = []
         for i, distribution in enumerate(child_distributions):
             child_list.append(distribution(split_inputs[i]))
@@ -193,7 +191,7 @@ class MultiActionDistribution(ActionDistribution):
 
     def logp(self, x):
         """The log-likelihood of the action distribution."""
-        split_list = self.reshaper.split_tensor(x)
+        split_list = tf.split(x, len(self.input_lens), axis=1)
         for i, distribution in enumerate(self.child_distributions):
             # Remove extra categorical dimension
             if isinstance(distribution, Categorical):
diff --git a/python/ray/rllib/models/catalog.py b/python/ray/rllib/models/catalog.py
index 71e181d10..b98061fdd 100644
--- a/python/ray/rllib/models/catalog.py
+++ b/python/ray/rllib/models/catalog.py
@@ -87,16 +87,17 @@ class ModelCatalog(object):
         elif isinstance(action_space, gym.spaces.Discrete):
             return Categorical, action_space.n
         elif isinstance(action_space, gym.spaces.Tuple):
-            size = 0
             child_dist = []
+            input_lens = []
             for action in action_space.spaces:
                 dist, action_size = ModelCatalog.get_action_dist(action)
                 child_dist.append(dist)
-                size += action_size
+                input_lens.append(action_size)
             return partial(
                 MultiActionDistribution,
                 child_distributions=child_dist,
-                action_space=action_space), size
+                action_space=action_space,
+                input_lens=input_lens), sum(input_lens)
 
         raise NotImplementedError("Unsupported args: {} {}".format(
             action_space, dist_type))
diff --git a/python/ray/rllib/models/multiagentfcnet.py b/python/ray/rllib/models/multiagentfcnet.py
index d000e95df..dad7f2983 100644
--- a/python/ray/rllib/models/multiagentfcnet.py
+++ b/python/ray/rllib/models/multiagentfcnet.py
@@ -6,7 +6,7 @@ import tensorflow as tf
 
 from ray.rllib.models.model import Model
 from ray.rllib.models.fcnet import FullyConnectedNetwork
-from ray.rllib.models.action_dist import Reshaper
+from ray.rllib.utils.reshaper import Reshaper
 
 
 class MultiAgentFullyConnectedNetwork(Model):