diff --git a/scripts/evaluate_models.py b/scripts/evaluate_models.py
index 3fe885a..422cc8a 100644
--- a/scripts/evaluate_models.py
+++ b/scripts/evaluate_models.py
@@ -483,6 +483,12 @@ def select_deterministic_action(model_name: str, agent, state: np.ndarray) -> np
     return action
 
 
+def select_no_control_action(env: SUMOEdgeVSLEnvironment) -> np.ndarray:
+    if env.num_controlled_edges <= 0:
+        return np.zeros(0, dtype=np.int64)
+    return np.full(env.num_controlled_edges, env.action_dim - 1, dtype=np.int64)
+
+
 def resolve_logged_action_info(
     model_name: str,
     env: SUMOEdgeVSLEnvironment,
@@ -491,7 +497,7 @@ def resolve_logged_action_info(
     edge_idx: int,
     action_speed_kmh: float,
 ) -> Tuple[int, float]:
-    if model_name == BASELINE_NAME or not action_applied_mask[edge_idx]:
+    if not action_applied_mask[edge_idx]:
         return -1, action_speed_kmh
     controlled_idx = edge_idx - env.controlled_edge_start_index
     if action is None or controlled_idx < 0 or controlled_idx >= len(action):
@@ -550,8 +556,8 @@ def evaluate_single_model(
 
     while True:
         if model_name == BASELINE_NAME:
-            action = None
-            next_state, reward, done, info = env.step(action=None, apply_control=False)
+            action = select_no_control_action(env)
+            next_state, reward, done, info = env.step(action, apply_control=True)
         else:
             action = select_deterministic_action(model_name, agent, state)
             next_state, reward, done, info = env.step(action, apply_control=True)