diff --git a/alpha_automl/automl_manager.py b/alpha_automl/automl_manager.py
index 875b98e1..9c60e998 100644
--- a/alpha_automl/automl_manager.py
+++ b/alpha_automl/automl_manager.py
@@ -82,7 +82,7 @@ def _search_pipelines(self, automl_hyperparams):
                     found_pipelines += 1
                     yield {'pipeline': alphaautoml_pipeline, 'message': 'SCORED'}
             except:
-                logger.info(f'Pipeline scoring error!')
+                logger.debug(f'Pipeline scoring error!')
                 continue
         
         logger.debug(f'Found {found_pipelines} pipelines')
diff --git a/alpha_automl/pipeline_search/agent_lab.py b/alpha_automl/pipeline_search/agent_lab.py
index 122311ca..862a8841 100644
--- a/alpha_automl/pipeline_search/agent_lab.py
+++ b/alpha_automl/pipeline_search/agent_lab.py
@@ -14,23 +14,20 @@
 logger = logging.getLogger(__name__)
 
 
-def pipeline_search_rllib(
-    game, time_bound, checkpoint_load_folder, checkpoint_save_folder
-):
+def pipeline_search_rllib(game, time_bound, checkpoint_load_folder, checkpoint_save_folder):
     """
     Search for pipelines using Rllib
     """
     ray.init(local_mode=True, num_cpus=8)
     num_cpus = int(ray.available_resources()["CPU"])
-    logger.debug("[RlLib] Ready")
 
     # load checkpoint or create a new one
     algo = load_rllib_checkpoint(game, checkpoint_load_folder, num_rollout_workers=7)
-    logger.debug("[RlLib] Create Algo object done")
+    logger.debug("Create Algo object done")
 
     # train model
     train_rllib_model(algo, time_bound, checkpoint_load_folder, checkpoint_save_folder)
-    logger.debug("[RlLib] Done")
+    logger.debug("Training done")
     ray.shutdown()
 
 
@@ -58,11 +55,11 @@ def load_rllib_checkpoint(game, checkpoint_load_folder, num_rollout_workers):
     )
     config.lr = 1e-5
     config.simple_optimizer = True
-    logger.debug("[RlLib] Create Config done")
+    logger.debug("Create Config done")
 
     # Checking if the list is empty or not
-    if contain_checkpoints(checkpoint_load_folder):
-        logger.debug("[RlLib] Cannot read RlLib checkpoint, create a new one.")
+    if not contain_checkpoints(checkpoint_load_folder):
+        logger.debug("Cannot read checkpoint, create a new one.")
         return config.build()
     else:
         algo = config.build()
@@ -88,7 +85,7 @@ def train_rllib_model(algo, time_bound, checkpoint_load_folder, checkpoint_save_
             or (best_unchanged_iter >= 600 and result["episode_reward_mean"] >= 0)
             # or result["episode_reward_mean"] >= 70
         ):
-            logger.debug(f"[RlLib] Train Timeout")
+            logger.debug(f"Training timeout reached")
             break
 
         if contain_checkpoints(checkpoint_save_folder):
@@ -110,7 +107,7 @@ def train_rllib_model(algo, time_bound, checkpoint_load_folder, checkpoint_save_
 
 
 def load_rllib_policy_weights(checkpoint_folder):
-    logger.debug(f"[RlLib] Synchronizing model weights...")
+    logger.debug(f"Synchronizing model weights...")
     policy = Policy.from_checkpoint(checkpoint_folder)
     policy = policy["default_policy"]
     weights = policy.get_weights()
@@ -125,7 +122,7 @@ def save_rllib_checkpoint(algo, checkpoint_save_folder):
     path_to_checkpoint = save_result.checkpoint.path
 
     logger.debug(
-        f"[RlLib] An Algorithm checkpoint has been created inside directory: '{path_to_checkpoint}'."
+        f"An Algorithm checkpoint has been created inside directory: '{path_to_checkpoint}'."
     )
 
 
@@ -192,8 +189,8 @@ def contain_checkpoints(folder_path):
     ):
         return True
     else:
-        logger.info(
-            f"[RlLib] Checkpoint folder {folder_path} does not contain all necessary files, files: {file_list}."
+        logger.debug(
+            f"Checkpoint folder {folder_path} does not contain all necessary files, files: {file_list}."
         )
 
     return False