diff --git a/alr_envs/alr/__init__.py b/alr_envs/alr/__init__.py
index 3d42b9c..9b0fb2f 100644
--- a/alr_envs/alr/__init__.py
+++ b/alr_envs/alr/__init__.py
@@ -400,11 +400,11 @@ register(id='TableTennis4DCtxt-v0',
 register(
         id='ALRBeerPong-v0',
         entry_point='alr_envs.alr.mujoco:ALRBeerBongEnv',
-        max_episode_steps=150,
+        max_episode_steps=600,
         kwargs={
             "rndm_goal": False,
             "cup_goal_pos": [0.1, -2.0],
-            "frameskip": 4
+            "frame_skip": 1
         }
     )
 
@@ -413,11 +413,11 @@ register(
 register(
         id='ALRBeerPong-v1',
         entry_point='alr_envs.alr.mujoco:ALRBeerBongEnv',
-        max_episode_steps=150,
+        max_episode_steps=600,
         kwargs={
             "rndm_goal": True,
             "cup_goal_pos": [-0.3, -1.2],
-            "frameskip": 4
+            "frame_skip": 1
         }
     )
 
diff --git a/alr_envs/alr/mujoco/beerpong/beerpong.py b/alr_envs/alr/mujoco/beerpong/beerpong.py
index 4f7b85f..508cfba 100644
--- a/alr_envs/alr/mujoco/beerpong/beerpong.py
+++ b/alr_envs/alr/mujoco/beerpong/beerpong.py
@@ -177,12 +177,7 @@ class ALRBeerBongEnv(MujocoEnv, utils.EzPickle):
 
     @property
     def dt(self):
-        return super(ALRBeerBongEnv, self).dt()*self.repeat_action
-
-class ALRBeerPongStepEnv(ALRBeerBongEnv):
-    def __init__(self, frame_skip=1, apply_gravity_comp=True, noisy=False,
-                 rndm_goal=False, cup_goal_pos=None):
-        super(ALRBeerPongStepEnv, self).__init__(frame_skip, apply_gravity_comp, noisy, rndm_goal, cup_goal_pos)
+        return super(ALRBeerBongEnv, self).dt*self.repeat_action
 
 if __name__ == "__main__":
     env = ALRBeerBongEnv(rndm_goal=True)
diff --git a/alr_envs/alr/mujoco/beerpong/new_mp_wrapper.py b/alr_envs/alr/mujoco/beerpong/new_mp_wrapper.py
index 9683687..2bdc11a 100644
--- a/alr_envs/alr/mujoco/beerpong/new_mp_wrapper.py
+++ b/alr_envs/alr/mujoco/beerpong/new_mp_wrapper.py
@@ -39,7 +39,6 @@ class NewMPWrapper(EpisodicWrapper):
     def _episode_callback(self, action: np.ndarray) -> Tuple[np.ndarray, Union[np.ndarray, None]]:
         if self.mp.learn_tau:
             self.env.env.release_step = action[0]/self.env.dt         # Tau value
-            # self.env.env.release_step = np.clip(action[0]/self.env.dt, 20, 260)        # Tau value
             return action, None
         else:
             return action, None