diff --git a/fancy_gym/black_box/black_box_wrapper.py b/fancy_gym/black_box/black_box_wrapper.py
index b99d138..d428184 100644
--- a/fancy_gym/black_box/black_box_wrapper.py
+++ b/fancy_gym/black_box/black_box_wrapper.py
@@ -219,11 +219,12 @@ class BlackBoxWrapper(gym.ObservationWrapper):
                 if self.render_kwargs:
                     self.env.render(**self.render_kwargs)
 
-                if done or self.replanning_schedule(self.current_pos, self.current_vel, obs, c_action,
-                                                    t + 1 + self.current_traj_steps):
+                if done or (self.replanning_schedule(self.current_pos, self.current_vel, obs, c_action,
+                                                        t + 1 + self.current_traj_steps)
+                        and self.max_planning_times is not None and self.plan_counts < self.max_planning_times):
 
-                    # if self.max_planning_times is not None and self.plan_counts >= self.max_planning_times:
-                    #     continue
+                        # if self.max_planning_times is not None and self.plan_counts >= self.max_planning_times:
+                        #     continue
 
                     self.condition_pos = pos if self.desired_conditioning else self.current_pos
                     self.condition_vel = vel if self.desired_conditioning else self.current_vel
diff --git a/fancy_gym/examples/example_replanning.py b/fancy_gym/examples/example_replanning.py
new file mode 100644
index 0000000..e69de29
diff --git a/fancy_gym/examples/examples_movement_primitives.py b/fancy_gym/examples/examples_movement_primitives.py
index 8d9cecc..1cda812 100644
--- a/fancy_gym/examples/examples_movement_primitives.py
+++ b/fancy_gym/examples/examples_movement_primitives.py
@@ -46,6 +46,7 @@ def example_mp(env_name="HoleReacherProMP-v0", seed=1, iterations=1, render=True
         # full observation space of the last step, if replanning/sub-trajectory learning is used. The 'reward' is equal
         # to the return of a trajectory. Default is the sum over the step-wise rewards.
         obs, reward, done, info = env.step(ac)
+        print(f'steps: {info["num_steps"][-1]}')
         # Aggregated returns
         returns += reward