Implement close

mujoco_maze/agent_model.py

@@ -17,6 +17,13 @@ class AgentModel(ABC, MujocoEnv, EzPickle):
         MujocoEnv.__init__(self, file_path, frame_skip)
         EzPickle.__init__(self)
 
+    def close(self):
+        if self.viewer is not None and hasattr(self.viewer, "window"):
+            import glfw
+
+            glfw.destroy_window(self.viewer.window)
+        super().close()
+
     @abstractmethod
     def _get_obs(self) -> np.ndarray:
         """Returns the observation from the model.

mujoco_maze/maze_env.py

@@ -453,3 +453,6 @@ class MazeEnv(gym.Env):
         done = self._task.termination(next_obs)
         info["position"] = self.wrapped_env.get_xy()
         return next_obs, inner_reward + outer_reward, done, info
+
+    def close(self):
+        self.wrapped_env.close()

tests/test_envs.py

@@ -24,7 +24,7 @@ def test_point_maze(maze_id):
 
 @pytest.mark.parametrize("v", [0, 1])
 def test_maze_args(v):
-    env = gym.make(f"PointTRoom-v{v}", task_kwargs={"goals": [(-2.0, 4.0)]})
+    env = gym.make(f"PointTRoom-v{v}", task_kwargs={"goals": [(-2.0, -4.0)]})
     assert env.reset().shape == (7,)
     s, _, _, _ = env.step(env.action_space.sample())
     assert s.shape == (7,)