diff --git a/experiments/PPOTrainer.py b/experiments/PPOTrainer.py
index 6e8b6f7..aea69d2 100644
--- a/experiments/PPOTrainer.py
+++ b/experiments/PPOTrainer.py
@@ -9,7 +9,6 @@ import jax
 import jax.numpy as jnp
 import numpy as np
 import optax
-import torch
 import tqdm
 from flax.training.train_state import TrainState
 from torch.utils.tensorboard import SummaryWriter
@@ -381,7 +380,9 @@ class PPOTrainer:
             self._ppo.update_ppo(self.agent_state, storage, self.key)
         )
 
-        avg_episodic_return = float(jnp.mean(jax.device_get(self.episode_stats.returned_episode_returns)))
+        avg_episodic_return = float(
+            jnp.mean(jax.device_get(self.episode_stats.returned_episode_returns))
+        )
 
         return (
             next_env_state,
diff --git a/experiments/_train_backup.py b/experiments/_train_backup.py
index 04a3df4..6d6a181 100644
--- a/experiments/_train_backup.py
+++ b/experiments/_train_backup.py
@@ -43,6 +43,7 @@ def make_env(config_path: str | None, num_envs: int) -> Callable:
 
     return thunk
 
+
 def save_model(model_path: str, agent_state: TrainState, args: PPOArgs):
     with open(model_path, "wb") as f:
         f.write(
@@ -198,7 +199,6 @@ def train(args: PPOArgs):
         value = critic.apply(agent_state.params["critic_params"], hidden_critic)
         return action, logprob, value.squeeze(-1), key
 
-
     # GAE
     @jax.jit
     def compute_gae_once(carry, inp, gamma, gae_lambda):
@@ -226,6 +226,7 @@ def train(args: PPOArgs):
             reverse=True,
         )
         return storage.replace(advantages=advantages, returns=advantages + storage.values)
+
     # END GAE
 
     # --- Main training loop ---
diff --git a/experiments/plots/__init__.py b/experiments/plots/__init__.py
index 5abf51e..92f34cb 100644
--- a/experiments/plots/__init__.py
+++ b/experiments/plots/__init__.py
@@ -1,3 +1,3 @@
 from .plot import simple_plot
 
-__all__ = ["simple_plot"]
\ No newline at end of file
+__all__ = ["simple_plot"]