diff --git a/fancy_gym/envs/__init__.py b/fancy_gym/envs/__init__.py
index a12f057..78e1a05 100644
--- a/fancy_gym/envs/__init__.py
+++ b/fancy_gym/envs/__init__.py
@@ -234,10 +234,7 @@ for reward_type in ["Dense", "TemporalSparse", "TemporalSpatialSparse"]:
     register(
         id='BoxPushing{}-v0'.format(reward_type),
         entry_point='fancy_gym.envs.mujoco:BoxPushing{}'.format(reward_type),
-        max_episode_steps=MAX_EPISODE_STEPS_BOX_PUSHING//10,  # divided by frames skip
-        kwargs={
-            "frame_skip": 10
-        }
+        max_episode_steps=MAX_EPISODE_STEPS_BOX_PUSHING,
     )
 
 # Here we use the same reward as in BeerPong-v0, but now consider after the release,
diff --git a/fancy_gym/envs/mujoco/box_pushing/box_pushing_env.py b/fancy_gym/envs/mujoco/box_pushing/box_pushing_env.py
index b5490aa..eea6455 100644
--- a/fancy_gym/envs/mujoco/box_pushing/box_pushing_env.py
+++ b/fancy_gym/envs/mujoco/box_pushing/box_pushing_env.py
@@ -9,7 +9,7 @@ from fancy_gym.envs.mujoco.box_pushing.box_pushing_utils import desired_rod_quat
 
 import mujoco
 
-MAX_EPISODE_STEPS_BOX_PUSHING = 1000
+MAX_EPISODE_STEPS_BOX_PUSHING = 100
 
 BOX_POS_BOUND = np.array([[0.3, -0.45, -0.01], [0.6, 0.45, -0.01]])
 
@@ -60,7 +60,7 @@ class BoxPushingEnvBase(MujocoEnv, utils.EzPickle):
         self._steps += 1
         self._episode_energy += np.sum(np.square(action))
 
-        episode_end = True if self._steps >= MAX_EPISODE_STEPS_BOX_PUSHING//self.frame_skip else False
+        episode_end = True if self._steps >= MAX_EPISODE_STEPS_BOX_PUSHING else False
 
         box_pos = self.data.body("box_0").xpos.copy()
         box_quat = self.data.body("box_0").xquat.copy()
@@ -121,8 +121,8 @@ class BoxPushingEnvBase(MujocoEnv, utils.EzPickle):
         return self._get_obs()
 
     def sample_context(self):
-        pos = np.random.uniform(low=BOX_POS_BOUND[0], high=BOX_POS_BOUND[1], size=3)
-        theta = np.random.uniform(low=0, high=np.pi * 2)
+        pos = self.np_random.uniform(low=BOX_POS_BOUND[0], high=BOX_POS_BOUND[1])
+        theta = self.np_random.uniform(low=0, high=np.pi * 2)
         quat = rot_to_quat(theta, np.array([0, 0, 1]))
         return np.concatenate([pos, quat])
 
@@ -360,7 +360,7 @@ class BoxPushingTemporalSpatialSparse(BoxPushingEnvBase):
 if __name__=="__main__":
     env = BoxPushingTemporalSpatialSparse(frame_skip=10)
     env.reset()
-    for i in range(100):
+    for i in range(1):
         env.reset()
         for _ in range(100):
             env.render("human")