diff --git a/assume/reinforcement_learning/algorithms/matd3.py b/assume/reinforcement_learning/algorithms/matd3.py
index 857bc4e0..0d900497 100644
--- a/assume/reinforcement_learning/algorithms/matd3.py
+++ b/assume/reinforcement_learning/algorithms/matd3.py
@@ -398,6 +398,21 @@ def update_policy(self):
 
         logger.debug("Updating Policy")
         n_rl_agents = len(self.learning_role.rl_strats.keys())
+
+        # Update noise decay and leanrng rate
+        updated_noise_decay = self.learning_role.noise_schedule(
+            self.learning_role.get_progress_remaining()
+        )
+
+        #loop again over all units to avoid update call for every gradient step, as it will be ambiguous
+        for u_id, unit_strategy in self.learning_role.rl_strats.items():
+            critic = self.learning_role.critics[u_id]
+            actor = self.learning_role.rl_strats[u_id].actor
+            unit_strategy.action_noise.update_noise_decay(updated_noise_decay)
+            # Update learning rate
+            self.update_learning_rate([critic.optimizer, actor.optimizer])
+
+
         for _ in range(self.gradient_steps):
             self.n_updates += 1
             i = 0
@@ -408,9 +423,7 @@ def update_policy(self):
                 actor = self.learning_role.rl_strats[u_id].actor
                 actor_target = self.learning_role.rl_strats[u_id].actor_target
 
-                # Update learning rate
-                self.update_learning_rate([critic.optimizer, actor.optimizer])
-
+                
                 if i % 100 == 0:
                     # only update target networks every 100 steps, to have delayed network update
                     transitions = self.learning_role.buffer.sample(self.batch_size)
@@ -531,9 +544,4 @@ def update_policy(self):
                     )
                 i += 1
 
-        # Update noise decay
-        updated_noise_decay = self.learning_role.noise_schedule(
-            self.learning_role.get_progress_remaining()
-        )
-        for unit_strategy in self.learning_role.rl_strats.values():
-            unit_strategy.action_noise.update_noise_decay(updated_noise_decay)
+        
\ No newline at end of file
diff --git a/assume/reinforcement_learning/learning_role.py b/assume/reinforcement_learning/learning_role.py
index c63ab6c4..de664d3f 100644
--- a/assume/reinforcement_learning/learning_role.py
+++ b/assume/reinforcement_learning/learning_role.py
@@ -87,6 +87,7 @@ def __init__(
         if use_lr_schedule:
             self.lr_schedule = get_schedule_fn(linear_schedule(self.learning_rate))
         else:
+            #linear schedule as no config item stating it should be sheduled is present
             self.lr_schedule = get_schedule_fn(self.learning_rate)
 
         noise_dt = learning_config.get("noise_dt", 1)
diff --git a/examples/inputs/example_02a/config.yaml b/examples/inputs/example_02a/config.yaml
index 8da7961f..588e8667 100644
--- a/examples/inputs/example_02a/config.yaml
+++ b/examples/inputs/example_02a/config.yaml
@@ -17,12 +17,13 @@ tiny:
     actor_architecture: mlp
     learning_rate: 0.001
     training_episodes: 10
-    episodes_collecting_initial_experience: 3
+    episodes_collecting_initial_experience: 1
     train_freq: 24h
     gradient_steps: -1
     batch_size: 64
     gamma: 0.99
     device: cpu
+    use_lr_schedule: True
     noise_sigma: 0.1
     noise_scale: 1
     noise_dt: 1
@@ -62,11 +63,12 @@ base:
     learning_rate: 0.001
     training_episodes: 50
     episodes_collecting_initial_experience: 5
-    train_freq: 24h
+    train_freq: 12h
     gradient_steps: -1
     batch_size: 256
     gamma: 0.99
     device: cpu
+    use_lr_schedule: True
     noise_sigma: 0.1
     noise_scale: 1
     noise_dt: 1