refactor MDP.computePR() code to do with rewards matrix to be more clear

mdp.py

@@ -560,7 +560,7 @@ class MDP(object):
         # check(P, R) has already been run and doesn't fail.
         
         # make P be an object array with (S, S) shaped array elements
-        if (P.dtype == object):
+        if (P.dtype is object):
             self.P = P
             self.A = self.P.shape[0]
             self.S = self.P[0].shape[0]
@@ -572,17 +572,18 @@ class MDP(object):
                 self.P[aa] = P[aa, :, :]
         
         # make R have the shape (S, A)
-        if ((R.ndim == 2) and (not R.dtype is object)):
-            # R already has shape (S, A)
-            self.R = R
-        else: 
-            # R has shape (A, S, S) or object shaped (A,) with each element
-            # shaped (S, S)
+        if R.dtype is object:
+            # R is object shaped (A,) with each element shaped (S, S)
             self.R = zeros((self.S, self.A))
-            if (R.dtype is object):
-                for aa in range(self.A):
-                    self.R[:, aa] = multiply(P[aa], R[aa]).sum(1)
+            for aa in range(self.A):
+                self.R[:, aa] = multiply(P[aa], R[aa]).sum(1)
+        else:
+            if R.ndim == 2:
+                # R already has shape (S, A)
+                self.R = R
             else:
+                # R has shape (A, S, S)
+                self.R = zeros((self.S, self.A))
                 for aa in range(self.A):
                     self.R[:, aa] = multiply(P[aa], R[aa, :, :]).sum(1)