have multi-discrete use same dtype as discrete, fixes #1204
diff --git a/gym/spaces/multi_discrete.py b/gym/spaces/multi_discrete.py
index b92173205e6..a8406e98dcf 100644
--- a/gym/spaces/multi_discrete.py
+++ b/gym/spaces/multi_discrete.py
@@ -1,4 +1,3 @@
-import gym
 import numpy as np
 from .space import Space
 
@@ -29,9 +28,9 @@ def __init__(self, nvec):
         nvec: vector of counts of each categorical variable
         """
         assert (np.array(nvec) > 0).all(), 'nvec (counts) have to be positive'
-        self.nvec = np.asarray(nvec, dtype=np.uint32)
+        self.nvec = np.asarray(nvec, dtype=np.int64)
 
-        super(MultiDiscrete, self).__init__(self.nvec.shape, np.uint32)
+        super(MultiDiscrete, self).__init__(self.nvec.shape, np.int64)
         self.np_random = np.random.RandomState()
 
     def seed(self, seed):
diff --git a/gym/spaces/space.py b/gym/spaces/space.py
index f12b631f171..d0f1483b5dc 100644
--- a/gym/spaces/space.py
+++ b/gym/spaces/space.py
@@ -1,6 +1,3 @@
-import numpy as np
-
-
 class Space(object):
     """Defines the observation and action spaces, so you can write generic
     code that applies to any Env. For example, you can choose a random
