Remove Robotics environments from Gym

gym/__init__.py

@@ -3,7 +3,6 @@
 
 from gym.core import (
     Env,
-    GoalEnv,
     Wrapper,
     ObservationWrapper,
     ActionWrapper,

gym/core.py

@@ -176,52 +176,6 @@ def __exit__(self, *args):
         return False
 
 
-class GoalEnv(Env):
-    """A goal-based environment. It functions just as any regular OpenAI Gym environment but it
-    imposes a required structure on the observation_space. More concretely, the observation
-    space is required to contain at least three elements, namely `observation`, `desired_goal`, and
-    `achieved_goal`. Here, `desired_goal` specifies the goal that the agent should attempt to achieve.
-    `achieved_goal` is the goal that it currently achieved instead. `observation` contains the
-    actual observations of the environment as per usual.
-    """
-
-    def reset(self, seed: Optional[int] = None):
-        super().reset(seed=seed)
-        # Enforce that each GoalEnv uses a Goal-compatible observation space.
-        if not isinstance(self.observation_space, gym.spaces.Dict):
-            raise error.Error(
-                "GoalEnv requires an observation space of type gym.spaces.Dict"
-            )
-        for key in ["observation", "achieved_goal", "desired_goal"]:
-            if key not in self.observation_space.spaces:
-                raise error.Error(
-                    'GoalEnv requires the "{}" key to be part of the observation dictionary.'.format(
-                        key
-                    )
-                )
-
-    @abstractmethod
-    def compute_reward(self, achieved_goal, desired_goal, info):
-        """Compute the step reward. This externalizes the reward function and makes
-        it dependent on a desired goal and the one that was achieved. If you wish to include
-        additional rewards that are independent of the goal, you can include the necessary values
-        to derive it in 'info' and compute it accordingly.
-
-        Args:
-            achieved_goal (object): the goal that was achieved during execution
-            desired_goal (object): the desired goal that we asked the agent to attempt to achieve
-            info (dict): an info dictionary with additional information
-
-        Returns:
-            float: The reward that corresponds to the provided achieved goal w.r.t. to the desired
-            goal. Note that the following should always hold true:
-
-                ob, reward, done, info = env.step()
-                assert reward == env.compute_reward(ob['achieved_goal'], ob['desired_goal'], info)
-        """
-        raise NotImplementedError
-
-
 class Wrapper(Env):
     """Wraps the environment to allow a modular transformation.