diff --git a/mujoco_maze/assets/ant.xml b/mujoco_maze/assets/ant.xml
index 5a49d7f..ac555ef 100755
--- a/mujoco_maze/assets/ant.xml
+++ b/mujoco_maze/assets/ant.xml
@@ -66,7 +66,6 @@
         </body>
       </body>
     </body>
-
   </worldbody>
   <actuator>
     <motor joint="hip_4" ctrlrange="-30.0 30.0" ctrllimited="true" />
diff --git a/mujoco_maze/assets/point.xml b/mujoco_maze/assets/point.xml
index cb3d0b8..5ad5f87 100755
--- a/mujoco_maze/assets/point.xml
+++ b/mujoco_maze/assets/point.xml
@@ -22,8 +22,6 @@
             <joint name='bally' type='slide' axis='0 1 0' pos='0 0 0' />
             <joint name='rot' type='hinge' axis='0 0 1' pos='0 0 0' limited="false" />
         </body>
-        <!-- Goal / target position -->
-        <geom name="target" type="sphere" size="0.5" pos="0 8 0" rgba="1. 0. 0. 0.6" />
     </worldbody>
     <actuator>
         <!-- Those are just dummy actuators for providing ranges -->
diff --git a/mujoco_maze/maze_env.py b/mujoco_maze/maze_env.py
index d658412..912f32f 100644
--- a/mujoco_maze/maze_env.py
+++ b/mujoco_maze/maze_env.py
@@ -263,9 +263,6 @@ class MazeEnv(gym.Env):
 
         _, file_path = tempfile.mkstemp(text=True, suffix=".xml")
         tree.write(file_path)
-
-        x, y, _ = map(float, tree.find(".//geom[@name='target']").attrib["pos"].split())
-        self.goal_xy = np.array([x, y])
         self.wrapped_env = self.MODEL_CLASS(*args, file_path=file_path, **kwargs)
 
     def get_ori(self):
@@ -532,23 +529,15 @@ class MazeEnv(gym.Env):
                         return True
         return False
 
-    def _is_in_goal_position(self, pos):
-        return np.linalg.norm(pos - self.goal_xy) <= 0.6
-
     def step(self, action):
         self.t += 1
-        goal_reward = 0.0
         if self._manual_collision:
             old_pos = self.wrapped_env.get_xy()
             inner_next_obs, inner_reward, done, info = self.wrapped_env.step(action)
             new_pos = self.wrapped_env.get_xy()
             if self._is_in_collision(new_pos):
                 self.wrapped_env.set_xy(old_pos)
-
-            if self._is_in_goal_position(new_pos):
-                goal_reward = 1.0
-                done = True
         else:
             inner_next_obs, inner_reward, done, info = self.wrapped_env.step(action)
         next_obs = self._get_obs()
-        return next_obs, inner_reward + goal_reward, done, info
+        return next_obs, inner_reward, False, info