diff --git a/alr_envs/examples/examples_movement_primitives.py b/alr_envs/examples/examples_movement_primitives.py
index 85dd6a1..755d912 100644
--- a/alr_envs/examples/examples_movement_primitives.py
+++ b/alr_envs/examples/examples_movement_primitives.py
@@ -36,11 +36,11 @@ def example_mp(env_name="HoleReacherProMP-v0", seed=1, iterations=1, render=True
             env.render(mode=None)
 
         # Now the action space is not the raw action but the parametrization of the trajectory generator,
-        # such as a ProMP
+        # such as a ProMP. You can still use it the same, though.
         ac = env.action_space.sample()
         # This executes a full trajectory
         obs, reward, done, info = env.step(ac)
-        # Aggregated reward
+        # Aggregated reward of trajectory
         rewards += reward
 
         if done:
@@ -62,9 +62,8 @@ def example_custom_mp(env_name="Reacher5dProMP-v0", seed=1, iterations=1, render
 
     """
     # Changing the arguments of the black box env is possible by providing them to gym as with all kwargs.
-    # E.g. here for way to many basis functions
-    # env = alr_envs.make(env_name, seed, basis_generator_kwargs={'num_basis': 1000})
-    env = alr_envs.make(env_name, seed)
+    # E.g. here for adding a lot of basis functions
+    env = alr_envs.make(env_name, seed, basis_generator_kwargs={'num_basis': 1000})
     # mp_dict.update({'black_box_kwargs': {'learn_sub_trajectories': True}})
     # mp_dict.update({'black_box_kwargs': {'do_replanning': lambda pos, vel, t: lambda t: t % 100}})