diff --git a/unit-4/main.py b/unit-4/main.py
index 347c250..834b615 100644
--- a/unit-4/main.py
+++ b/unit-4/main.py
@@ -69,7 +69,7 @@ class CartpolePolicy(nn.Module):
 
 class PixelcopterPolicy(nn.Module):
     def __init__(self, s_size, a_size, h_size, device):
-        super(Policy, self).__init__()
+        super(PixelcopterPolicy, self).__init__()
         self.fc1 = nn.Linear(s_size, h_size)
         self.fc2 = nn.Linear(h_size, h_size * 2)
         self.fc3 = nn.Linear(h_size * 2, a_size)
@@ -170,8 +170,29 @@ def reinforce(policy, env, optimizer, n_training_episodes, max_t, gamma, print_e
 
     return scores
 
-
-def push_to_hub(repo_id, model, hyperparameters, eval_env, video_fps=30):
+def record_video(env, policy, out_directory, fps=30):
+    """
+    Generate a replay video of the agent
+    :param env
+    :param Qtable: Qtable of our agent
+    :param out_directory
+    :param fps: how many frame per seconds (with taxi-v3 and frozenlake-v1 we use 1)
+    """
+    images = []
+    done = False
+    state = env.reset()
+    img = env.render(mode="rgb_array")
+    images.append(img)
+    while not done:
+        # Take the action (index) that have the maximum expected future reward given that state
+        action, _ = policy.act(state)
+        state, reward, done, info = env.step(action)  # We directly put next_state = state for recording logic
+        img = env.render(mode="rgb_array")
+        images.append(img)
+    imageio.mimsave(out_directory, [np.array(img) for i, img in enumerate(images)], fps=fps)
+
+
+def push_to_hub(repo_id, model, hparams, eval_env, video_fps=30):
     """
     Evaluate, Generate a video and Upload a model to Hugging Face Hub.
     This method does the complete pipeline:
@@ -182,7 +203,7 @@ def push_to_hub(repo_id, model, hyperparameters, eval_env, video_fps=30):
 
     :param repo_id: repo_id: id of the model repository from the Hugging Face Hub
     :param model: the pytorch model we want to save
-    :param hyperparameters: training hyperparameters
+    :param hparams: training hparams
     :param eval_env: evaluation environment
     :param video_fps: how many frame per seconds to record our video replay
     """
@@ -202,15 +223,15 @@ def push_to_hub(repo_id, model, hyperparameters, eval_env, video_fps=30):
         # Step 2: Save the model
         torch.save(model, local_directory / "model.pt")
 
-        # Step 3: Save the hyperparameters to JSON
-        with open(local_directory / "hyperparameters.json", "w") as outfile:
-            json.dump(hyperparameters, outfile)
+        # Step 3: Save the hparams to JSON
+        with open(local_directory / "hparams.json", "w") as outfile:
+            json.dump(hparams, outfile)
 
         # Step 4: Evaluate the model and build JSON
         mean_reward, std_reward = evaluate_agent(
             eval_env,
-            hyperparameters["max_t"],
-            hyperparameters["n_evaluation_episodes"],
+            hparams["max_t"],
+            hparams["n_evaluation_episodes"],
             model,
         )
         # Get datetime
@@ -218,9 +239,9 @@ def push_to_hub(repo_id, model, hyperparameters, eval_env, video_fps=30):
         eval_form_datetime = eval_datetime.isoformat()
 
         evaluate_data = {
-            "env_id": hyperparameters["env_id"],
+            "env_id": hparams["env_id"],
             "mean_reward": mean_reward,
-            "n_evaluation_episodes": hyperparameters["n_evaluation_episodes"],
+            "n_evaluation_episodes": hparams["n_evaluation_episodes"],
             "eval_datetime": eval_form_datetime,
         }
 
@@ -229,7 +250,7 @@ def push_to_hub(repo_id, model, hyperparameters, eval_env, video_fps=30):
             json.dump(evaluate_data, outfile)
 
         # Step 5: Create the model card
-        env_name = hyperparameters["env_id"]
+        env_name = hparams["env_id"]
 
         metadata = {}
         metadata["tags"] = [
@@ -256,8 +277,8 @@ def push_to_hub(repo_id, model, hyperparameters, eval_env, video_fps=30):
         metadata = {**metadata, **eval}
 
         model_card = f"""
-  # **Reinforce** Agent playing **{env_id}**
-  This is a trained model of a **Reinforce** agent playing **{env_id}** .
+  # **Reinforce** Agent playing **{env_name}**
+  This is a trained model of a **Reinforce** agent playing **{env_name}** .
   To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
   """
 
@@ -277,7 +298,7 @@ def push_to_hub(repo_id, model, hyperparameters, eval_env, video_fps=30):
 
         # Step 6: Record a video
         video_path = local_directory / "replay.mp4"
-        record_video(env, model, video_path, video_fps)
+        record_video(eval_env, model, video_path, video_fps)
 
         # Step 7. Push everything to the Hub
         api.upload_folder(