diff --git a/test/test_black_box.py b/test/test_black_box.py
index 139b1c2..76bd73e 100644
--- a/test/test_black_box.py
+++ b/test/test_black_box.py
@@ -4,7 +4,7 @@ from typing import Tuple, Type, Union, Optional, Callable
 import gymnasium as gym
 import numpy as np
 import pytest
-from gymnasium import register
+from gymnasium import register, make
 from gymnasium.core import ActType, ObsType
 
 import fancy_gym
@@ -13,7 +13,7 @@ from fancy_gym.utils.wrappers import TimeAwareObservation
 from test.utils import ugly_hack_to_mitigate_metaworld_bug
 
 SEED = 1
-ENV_IDS = ['Reacher5d-v0', 'dmc:ball_in_cup-catch-v0', 'metaworld:reach-v2', 'Reacher-v2']
+ENV_IDS = ['Reacher5d-v0', 'dm_control/ball_in_cup-catch-v0', 'metaworld/reach-v2', 'Reacher-v2']
 WRAPPERS = [fancy_gym.envs.mujoco.reacher.MPWrapper, fancy_gym.dmc.suite.ball_in_cup.MPWrapper,
             fancy_gym.meta.goal_object_change_mp_wrapper.MPWrapper, fancy_gym.open_ai.mujoco.reacher_v2.MPWrapper]
 ALL_MP_ENVS = chain(*fancy_gym.ALL_MOVEMENT_PRIMITIVE_ENVIRONMENTS.values())
@@ -102,7 +102,7 @@ def test_verbosity(mp_type: str, env_wrap: Tuple[str, Type[RawInterfaceWrapper]]
     _obs, _reward, _terminated, _truncated, info = env.step(env.action_space.sample())
     info_keys = list(info.keys())
 
-    env_step = fancy_gym.make(env_id, SEED)
+    env_step = make(env_id)
     env_step.reset()
     _obs, _reward, _terminated, _truncated, info = env.step(env.action_space.sample())
     info_keys_step = info.keys()
@@ -161,7 +161,7 @@ def test_context_space(mp_type: str, env_wrap: Tuple[str, Type[RawInterfaceWrapp
                             {'phase_generator_type': 'exp'},
                             {'basis_generator_type': 'rbf'})
     # check if observation space matches with the specified mask values which are true
-    env_step = fancy_gym.make(env_id, SEED)
+    env_step = make(env_id)
     wrapper = wrapper_class(env_step)
     assert env.observation_space.shape == wrapper.context_mask[wrapper.context_mask].shape
 
@@ -231,8 +231,9 @@ def test_learn_tau(mp_type: str, tau: float):
                              'learn_delay': False
                              },
                             {'basis_generator_type': basis_generator_type,
-                             }, seed=SEED)
+                             })
 
+    env.reset(seed=SEED)
     done = True
     for i in range(5):
         if done:
@@ -277,8 +278,9 @@ def test_learn_delay(mp_type: str, delay: float):
                              'learn_delay': True
                              },
                             {'basis_generator_type': basis_generator_type,
-                             }, seed=SEED)
+                             })
 
+    env.reset(seed=SEED)
     done = True
     for i in range(5):
         if done:
@@ -323,7 +325,9 @@ def test_learn_tau_and_delay(mp_type: str, tau: float, delay: float):
                              'learn_delay': True
                              },
                             {'basis_generator_type': basis_generator_type,
-                             }, seed=SEED)
+                             })
+
+    env.reset(seed=SEED)
 
     if env.spec.max_episode_steps * env.dt < delay + tau:
         return