Clarify relationship between reset() and RNGs (openai#2019)

The current docstring for `reset()` seems to indicate that the
environment will be identical after separate calls to `reset()`.
However, the `reset()` function isn't supposed to reset the states of
the environment's RNGs [1]. This change clarifies the relationship
between the `reset()` function and the RNGs.

[1]: openai#250

gym/core.py

@@ -56,7 +56,14 @@ def step(self, action):
         raise NotImplementedError
 
     def reset(self):
-        """Resets the state of the environment and returns an initial observation.
+        """Resets the environment to an initial state and returns an initial
+        observation.
+
+        Note that this function should not reset the environment's random
+        number generator(s); random variables in the environment's state should
+        be sampled independently between multiple calls to `reset()`. In other
+        words, each call of `reset()` should yield an environment suitable for
+        a new episode, independent of previous episodes.
 
         Returns:
             observation (object): the initial observation.