fix circular import (py2) by not importing spaces (#858)

gym/spaces/box.py

@@ -1,7 +1,8 @@
 import numpy as np
-from gym import Space, spaces, logger
+import gym
+from gym import logger
 
-class Box(Space):
+class Box(gym.Space):
     """
     A box in R^n.
     I.e., each coordinate is bounded.
@@ -30,10 +31,10 @@ class Box(Space):
             logger.warn("gym.spaces.Box autodetected dtype as %s. Please provide explicit dtype." % dtype)
         self.low = low.astype(dtype)
         self.high = high.astype(dtype)
-        Space.__init__(self, shape, dtype)
+        gym.Space.__init__(self, shape, dtype)
 
     def sample(self):
-        return spaces.np_random.uniform(low=self.low, high=self.high + (0 if self.dtype.kind == 'f' else 1), size=self.low.shape).astype(self.dtype)
+        return gym.spaces.np_random.uniform(low=self.low, high=self.high + (0 if self.dtype.kind == 'f' else 1), size=self.low.shape).astype(self.dtype)
     def contains(self, x):
         return x.shape == self.shape and (x >= self.low).all() and (x <= self.high).all()
 

gym/spaces/dict_space.py

@@ -1,7 +1,7 @@
-from gym import Space
+import gym
 from collections import OrderedDict
 
-class Dict(Space):
+class Dict(gym.Space):
     """
     A dictionary of simpler spaces.
 
@@ -36,7 +36,7 @@ class Dict(Space):
         if isinstance(spaces, list):
             spaces = OrderedDict(spaces)
         self.spaces = spaces
-        Space.__init__(self, None, None) # None for shape and dtype, since it'll require special handling
+        gym.Space.__init__(self, None, None) # None for shape and dtype, since it'll require special handling
 
     def sample(self):
         return OrderedDict([(k, space.sample()) for k, space in self.spaces.items()])

gym/spaces/discrete.py

@@ -1,7 +1,7 @@
 import numpy as np
-from gym import Space, spaces
+import gym
 
-class Discrete(Space):
+class Discrete(gym.Space):
     """
     {0,1,...,n-1}
 
@@ -10,9 +10,9 @@ class Discrete(Space):
     """
     def __init__(self, n):
         self.n = n
-        Space.__init__(self, (), np.int64)
+        gym.Space.__init__(self, (), np.int64)
     def sample(self):
-        return spaces.np_random.randint(self.n)
+        return gym.spaces.np_random.randint(self.n)
     def contains(self, x):
         if isinstance(x, int):
             as_int = x

gym/spaces/multi_binary.py

@@ -1,12 +1,12 @@
-from gym import spaces, Space
+import gym
 import numpy as np
 
-class MultiBinary(Space):
+class MultiBinary(gym.Space):
     def __init__(self, n):
         self.n = n
-        Space.__init__(self, (self.n,), np.int8)
+        gym.Space.__init__(self, (self.n,), np.int8)
     def sample(self):
-        return spaces.np_random.randint(low=0, high=2, size=self.n).astype(self.dtype)
+        return gym.spaces.np_random.randint(low=0, high=2, size=self.n).astype(self.dtype)
     def contains(self, x):
         return ((x==0) | (x==1)).all()
     def to_jsonable(self, sample_n):

gym/spaces/multi_discrete.py

@@ -1,17 +1,16 @@
 import gym
-from gym import spaces, Space
 import numpy as np
 
-class MultiDiscrete(Space):
+class MultiDiscrete(gym.Space):
     def __init__(self, nvec):
         """
         nvec: vector of counts of each categorical variable
         """
         self.nvec = np.asarray(nvec, dtype=np.int32)
         assert self.nvec.ndim == 1, 'nvec should be a 1d array (or list) of ints'
-        Space.__init__(self, (self.nvec.size,), np.int8)
+        gym.Space.__init__(self, (self.nvec.size,), np.int8)
     def sample(self):
-        return (spaces.np_random.rand(self.nvec.size) * self.nvec).astype(self.dtype)
+        return (gym.spaces.np_random.rand(self.nvec.size) * self.nvec).astype(self.dtype)
     def contains(self, x):
         return (x < self.nvec).all() and x.dtype.kind in 'ui'
     def to_jsonable(self, sample_n):

gym/spaces/tuple_space.py

@@ -1,6 +1,6 @@
-from gym import Space
+import gym
 
-class Tuple(Space):
+class Tuple(gym.Space):
     """
     A tuple (i.e., product) of simpler spaces
 
@@ -9,7 +9,7 @@ class Tuple(Space):
     """
     def __init__(self, spaces):
         self.spaces = spaces
-        Space.__init__(self, None, None)
+        gym.Space.__init__(self, None, None)
 
     def sample(self):
         return tuple([space.sample() for space in self.spaces])