diff --git a/benchmarl/conf/experiment/base_experiment.yaml b/benchmarl/conf/experiment/base_experiment.yaml
index 89566850..30d5d0ef 100644
--- a/benchmarl/conf/experiment/base_experiment.yaml
+++ b/benchmarl/conf/experiment/base_experiment.yaml
@@ -87,6 +87,8 @@ evaluation_deterministic_actions: True
 
 # List of loggers to use, options are: wandb, csv, tensorboard, mflow
 loggers: []
+# Wandb project name
+project_name: "benchmarl"
 # Create a json folder as part of the output in the format of marl-eval
 create_json: True
 
diff --git a/benchmarl/experiment/experiment.py b/benchmarl/experiment/experiment.py
index 12fa8859..719c8a27 100644
--- a/benchmarl/experiment/experiment.py
+++ b/benchmarl/experiment/experiment.py
@@ -94,6 +94,7 @@ class ExperimentConfig:
     evaluation_deterministic_actions: bool = MISSING
 
     loggers: List[str] = MISSING
+    project_name: str = MISSING
     create_json: bool = MISSING
 
     save_folder: Optional[str] = MISSING
@@ -535,6 +536,7 @@ def _setup_name(self):
 
     def _setup_logger(self):
         self.logger = Logger(
+            project_name=self.config.project_name,
             experiment_name=self.name,
             folder_name=str(self.folder_name),
             experiment_config=self.config,
diff --git a/benchmarl/experiment/logger.py b/benchmarl/experiment/logger.py
index 740389fd..6ed16ae4 100644
--- a/benchmarl/experiment/logger.py
+++ b/benchmarl/experiment/logger.py
@@ -33,6 +33,7 @@ def __init__(
         model_name: str,
         group_map: Dict[str, List[str]],
         seed: int,
+        project_name: str,
     ):
         self.experiment_config = experiment_config
         self.algorithm_name = algorithm_name
@@ -63,7 +64,7 @@ def __init__(
                     experiment_name=experiment_name,
                     wandb_kwargs={
                         "group": task_name,
-                        "project": "benchmarl",
+                        "project": project_name,
                         "id": experiment_name,
                     },
                 )
@@ -165,9 +166,11 @@ def log_evaluation(
             return
         to_log = {}
         json_metrics = {}
+        max_length_rollout_0 = 0
         for group in self.group_map.keys():
             # Cut the rollouts at the first done
-            for k, r in enumerate(rollouts):
+            rollouts_group = []
+            for i, r in enumerate(rollouts):
                 next_done = self._get_done(group, r)
                 # Reduce it to batch size
                 next_done = next_done.sum(
@@ -178,19 +181,23 @@ def log_evaluation(
                 done_index = next_done.nonzero(as_tuple=True)[0]
                 if done_index.numel() > 0:
                     done_index = done_index[0]
-                    rollouts[k] = r[: done_index + 1]
+                    r = r[: done_index + 1]
+                if i == 0:
+                    max_length_rollout_0 = max(r.batch_size[0], max_length_rollout_0)
+                rollouts_group.append(r)
 
             returns = [
-                self._get_reward(group, td).sum(0).mean().item() for td in rollouts
+                self._get_reward(group, td).sum(0).mean().item()
+                for td in rollouts_group
             ]
             json_metrics[group + "_return"] = torch.tensor(
-                returns, device=rollouts[0].device
+                returns, device=rollouts_group[0].device
             )
             to_log.update(
                 {
                     f"eval/{group}/reward/episode_reward_min": min(returns),
                     f"eval/{group}/reward/episode_reward_mean": sum(returns)
-                    / len(rollouts),
+                    / len(rollouts_group),
                     f"eval/{group}/reward/episode_reward_max": max(returns),
                 }
             )
@@ -223,10 +230,8 @@ def log_evaluation(
                     )
 
         self.log(to_log, step=step)
-        if video_frames is not None and rollouts[0].batch_size[0] > 1:
-            video_frames = np.stack(
-                video_frames[: rollouts[0].batch_size[0] - 1], axis=0
-            )
+        if video_frames is not None and max_length_rollout_0 > 1:
+            video_frames = np.stack(video_frames[: max_length_rollout_0 - 1], axis=0)
             vid = torch.tensor(
                 np.transpose(video_frames, (0, 3, 1, 2)),
                 dtype=torch.uint8,