diff --git a/freqtrade/freqai/RL/BaseReinforcementLearningModel.py b/freqtrade/freqai/RL/BaseReinforcementLearningModel.py
index d3395219a..e2c0f5fda 100644
--- a/freqtrade/freqai/RL/BaseReinforcementLearningModel.py
+++ b/freqtrade/freqai/RL/BaseReinforcementLearningModel.py
@@ -16,13 +16,13 @@ from pandas import DataFrame
 from stable_baselines3.common.callbacks import EvalCallback
 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.vec_env import SubprocVecEnv, VecMonitor
 
 from freqtrade.exceptions import OperationalException
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
 from freqtrade.freqai.freqai_interface import IFreqaiModel
 from freqtrade.freqai.RL.Base5ActionRLEnv import Actions, Base5ActionRLEnv
-from freqtrade.freqai.RL.BaseEnvironment import BaseActions, Positions
+from freqtrade.freqai.RL.BaseEnvironment import BaseActions, BaseEnvironment, Positions
 from freqtrade.freqai.RL.TensorboardCallback import TensorboardCallback
 from freqtrade.persistence import Trade
 
@@ -46,8 +46,8 @@ class BaseReinforcementLearningModel(IFreqaiModel):
             'cpu_count', 1), max(int(self.max_system_threads / 2), 1))
         th.set_num_threads(self.max_threads)
         self.reward_params = self.freqai_info['rl_config']['model_reward_parameters']
-        self.train_env: Union[SubprocVecEnv, Type[gym.Env]] = gym.Env()
-        self.eval_env: Union[SubprocVecEnv, Type[gym.Env]] = gym.Env()
+        self.train_env: Union[VecMonitor, SubprocVecEnv, gym.Env] = gym.Env()
+        self.eval_env: Union[VecMonitor, SubprocVecEnv, gym.Env] = gym.Env()
         self.eval_callback: Optional[EvalCallback] = None
         self.model_type = self.freqai_info['rl_config']['model_type']
         self.rl_config = self.freqai_info['rl_config']
@@ -431,7 +431,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
             return 0.
 
 
-def make_env(MyRLEnv: Type[gym.Env], env_id: str, rank: int,
+def make_env(MyRLEnv: Type[BaseEnvironment], env_id: str, rank: int,
              seed: int, train_df: DataFrame, price: DataFrame,
              env_info: Dict[str, Any] = {}) -> Callable:
     """
diff --git a/freqtrade/freqai/RL/TensorboardCallback.py b/freqtrade/freqai/RL/TensorboardCallback.py
index 7f8c76956..c5511cf53 100644
--- a/freqtrade/freqai/RL/TensorboardCallback.py
+++ b/freqtrade/freqai/RL/TensorboardCallback.py
@@ -3,6 +3,7 @@ from typing import Any, Dict, Type, Union
 
 from stable_baselines3.common.callbacks import BaseCallback
 from stable_baselines3.common.logger import HParam
+from stable_baselines3.common.vec_env import SubprocVecEnv
 
 from freqtrade.freqai.RL.BaseEnvironment import BaseActions, BaseEnvironment
 
@@ -16,7 +17,7 @@ class TensorboardCallback(BaseCallback):
         super().__init__(verbose)
         self.model: Any = None
         self.logger = None  # type: Any
-        self.training_env: BaseEnvironment = None  # type: ignore
+        self.training_env: Type[BaseEnvironment] = None  # type: ignore
         self.actions: Type[Enum] = actions
 
     def _on_training_start(self) -> None:
@@ -44,7 +45,10 @@ class TensorboardCallback(BaseCallback):
     def _on_step(self) -> bool:
 
         local_info = self.locals["infos"][0]
-        tensorboard_metrics = self.training_env.get_attr("tensorboard_metrics")[0]
+        if isinstance(self.training_env, SubprocVecEnv):
+            tensorboard_metrics = self.training_env.get_attr("tensorboard_metrics")[0]
+        else:
+            tensorboard_metrics = self.training_env.tensorboard_metrics
 
         for metric in local_info:
             if metric not in ["episode", "terminal_observation"]:
diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py
index 7eaaeab3e..ebc69452a 100644
--- a/freqtrade/freqai/freqai_interface.py
+++ b/freqtrade/freqai/freqai_interface.py
@@ -242,8 +242,8 @@ class IFreqaiModel(ABC):
                         new_trained_timerange, pair, strategy, dk, data_load_timerange
                     )
                 except Exception as msg:
-                    logger.warning(f"Training {pair} raised exception {msg.__class__.__name__}. "
-                                   f"Message: {msg}, skipping.")
+                    logger.exception(f"Training {pair} raised exception {msg.__class__.__name__}. "
+                                     f"Message: {msg}, skipping.")
 
                 self.train_timer('stop', pair)