Update box.py

gym/spaces/box.py

@@ -1,51 +1,48 @@
 import numpy as np
 
-import gym
-from gym import logger
 from .space import Space
 
 
 class Box(Space):
-    """
-    A box in R^n.
-    I.e., each coordinate is bounded.
+    """A box in R^n, i.e.each coordinate is bounded.
+    
+    There are two common use cases:
+    
+    * Identical bound for each dimension::
+        >>> Box(low=-1.0, high=2.0, shape=(3, 4), dtype=np.float32)
+        Box(3, 4)
+        
+    * Independent bound for each dimension::
+        >>> Box(low=np.array([-1.0, -2.0]), high=np.array([2.0, 4.0]), dtype=np.float32)
+        Box(2,)
 
-    Example usage:
-    self.action_space = spaces.Box(low=-10, high=10, shape=(1,))
     """
-    def __init__(self, low=None, high=None, shape=None, dtype=None):
-        """
-        Two kinds of valid input:
-            Box(low=-1.0, high=1.0, shape=(3,4)) # low and high are scalars, and shape is provided
-            Box(low=np.array([-1.0,-2.0]), high=np.array([2.0,4.0])) # low and high are arrays of the same shape
-        """
+    def __init__(self, low, high, shape=None, dtype=np.float32):
+        assert dtype is not None, 'dtype must be explicitly provided. '
+        self.dtype = np.dtype(dtype)
+
         if shape is None:
             assert low.shape == high.shape
-            shape = low.shape
+            self.shape = low.shape
+            self.low = low
+            self.high = high
         else:
             assert np.isscalar(low) and np.isscalar(high)
+            self.shape = tuple(shape)
+            self.low = np.full(self.shape, low)
+            self.high = np.full(self.shape, high)
             low = low + np.zeros(shape)
             high = high + np.zeros(shape)
-        if dtype is None:  # Autodetect type
-            if (high == 255).all():
-                dtype = np.uint8
-            else:
-                dtype = np.float32
-            logger.warn("gym.spaces.Box autodetected dtype as {}. Please provide explicit dtype.".format(dtype))
-        self.low = low.astype(dtype)
-        self.high = high.astype(dtype)
-        super(Box, self).__init__(shape, dtype)
-        self.np_random = np.random.RandomState()
-
-    def seed(self, seed):
-        self.np_random.seed(seed)
+        self.low = self.low.astype(self.dtype)
+        self.high = self.high.astype(self.dtype)
+        super(Box, self).__init__(self.shape, self.dtype)
 
     def sample(self):
         high = self.high if self.dtype.kind == 'f' else self.high.astype('int64') + 1
-        return self.np_random.uniform(low=self.low, high=high, size=self.low.shape).astype(self.dtype)
+        return self.np_random.uniform(low=self.low, high=high, size=self.shape).astype(self.dtype)
 
     def contains(self, x):
-        return x.shape == self.shape and (x >= self.low).all() and (x <= self.high).all()
+        return x.shape == self.shape and np.all(x >= self.low) and np.all(x <= self.high)
 
     def to_jsonable(self, sample_n):
         return np.array(sample_n).tolist()