diff --git a/freqtrade/freqai/RL/BaseReinforcementLearningModel.py b/freqtrade/freqai/RL/BaseReinforcementLearningModel.py
index 15acde6fb..b9b6cdd96 100644
--- a/freqtrade/freqai/RL/BaseReinforcementLearningModel.py
+++ b/freqtrade/freqai/RL/BaseReinforcementLearningModel.py
@@ -12,12 +12,11 @@ import pandas as pd
 import torch as th
 import torch.multiprocessing
 from pandas import DataFrame
-from stable_baselines3.common.callbacks import EvalCallback
-from stable_baselines3.common.callbacks import BaseCallback
+from stable_baselines3.common.callbacks import BaseCallback, EvalCallback
+from stable_baselines3.common.logger import HParam
 from stable_baselines3.common.monitor import Monitor
 from stable_baselines3.common.utils import set_random_seed
 from stable_baselines3.common.vec_env import SubprocVecEnv
-from stable_baselines3.common.logger import HParam
 
 from freqtrade.exceptions import OperationalException
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
@@ -157,7 +156,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
         self.eval_callback = EvalCallback(self.eval_env, deterministic=True,
                                           render=False, eval_freq=len(train_df),
                                           best_model_save_path=str(dk.data_path))
-        
+
         self.tensorboard_callback = TensorboardCallback()
 
     @abstractmethod
@@ -403,6 +402,7 @@ def make_env(MyRLEnv: Type[gym.Env], env_id: str, rank: int,
     set_random_seed(seed)
     return _init
 
+
 class TensorboardCallback(BaseCallback):
     """
     Custom callback for plotting additional values in tensorboard.
@@ -422,7 +422,7 @@ class TensorboardCallback(BaseCallback):
         metric_dict = {
             "eval/mean_reward": 0,
             "rollout/ep_rew_mean": 0,
-            "rollout/ep_len_mean":0 ,
+            "rollout/ep_len_mean": 0,
             "train/value_loss": 0,
             "train/explained_variance": 0,
         }
@@ -431,19 +431,21 @@ class TensorboardCallback(BaseCallback):
             HParam(hparam_dict, metric_dict),
             exclude=("stdout", "log", "json", "csv"),
         )
-    
+
     def _on_step(self) -> bool:
         custom_info = self.training_env.get_attr("custom_info")[0]
-        self.logger.record(f"_state/position", self.locals["infos"][0]["position"])
-        self.logger.record(f"_state/trade_duration", self.locals["infos"][0]["trade_duration"])
-        self.logger.record(f"_state/current_profit_pct", self.locals["infos"][0]["current_profit_pct"])
-        self.logger.record(f"_reward/total_profit", self.locals["infos"][0]["total_profit"])
-        self.logger.record(f"_reward/total_reward", self.locals["infos"][0]["total_reward"])
-        self.logger.record_mean(f"_reward/mean_trade_duration", self.locals["infos"][0]["trade_duration"])
-        self.logger.record(f"_actions/action", self.locals["infos"][0]["action"])
-        self.logger.record(f"_actions/_Invalid", custom_info["Invalid"])
-        self.logger.record(f"_actions/_Unknown", custom_info["Unknown"])
-        self.logger.record(f"_actions/Hold", custom_info["Hold"])
+        self.logger.record("_state/position", self.locals["infos"][0]["position"])
+        self.logger.record("_state/trade_duration", self.locals["infos"][0]["trade_duration"])
+        self.logger.record("_state/current_profit_pct", self.locals["infos"]
+                           [0]["current_profit_pct"])
+        self.logger.record("_reward/total_profit", self.locals["infos"][0]["total_profit"])
+        self.logger.record("_reward/total_reward", self.locals["infos"][0]["total_reward"])
+        self.logger.record_mean("_reward/mean_trade_duration", self.locals["infos"]
+                                [0]["trade_duration"])
+        self.logger.record("_actions/action", self.locals["infos"][0]["action"])
+        self.logger.record("_actions/_Invalid", custom_info["Invalid"])
+        self.logger.record("_actions/_Unknown", custom_info["Unknown"])
+        self.logger.record("_actions/Hold", custom_info["Hold"])
         for action in Actions:
             self.logger.record(f"_actions/{action.name}", custom_info[action.name])
-        return True
\ No newline at end of file
+        return True