diff --git a/freqtrade/freqai/RL/BaseReinforcementLearningModel.py b/freqtrade/freqai/RL/BaseReinforcementLearningModel.py
index 8ee3c7c56..642a9edf2 100644
--- a/freqtrade/freqai/RL/BaseReinforcementLearningModel.py
+++ b/freqtrade/freqai/RL/BaseReinforcementLearningModel.py
@@ -13,7 +13,8 @@ import pandas as pd
 import torch as th
 import torch.multiprocessing
 from pandas import DataFrame
-from stable_baselines3.common.callbacks import EvalCallback
+from sb3_contrib.common.maskable.callbacks import MaskableEvalCallback
+from sb3_contrib.common.maskable.utils import is_masking_supported
 from stable_baselines3.common.monitor import Monitor
 from stable_baselines3.common.utils import set_random_seed
 from stable_baselines3.common.vec_env import SubprocVecEnv, VecMonitor
@@ -48,7 +49,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
         self.reward_params = self.freqai_info['rl_config']['model_reward_parameters']
         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.eval_callback: Optional[MaskableEvalCallback] = None
         self.model_type = self.freqai_info['rl_config']['model_type']
         self.rl_config = self.freqai_info['rl_config']
         self.df_raw: DataFrame = DataFrame()
@@ -151,9 +152,11 @@ class BaseReinforcementLearningModel(IFreqaiModel):
 
         self.train_env = self.MyRLEnv(df=train_df, prices=prices_train, **env_info)
         self.eval_env = Monitor(self.MyRLEnv(df=test_df, prices=prices_test, **env_info))
-        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.eval_callback = MaskableEvalCallback(self.eval_env, deterministic=True,
+                                                  render=False, eval_freq=len(train_df),
+                                                  best_model_save_path=str(dk.data_path),
+                                                  use_masking=(self.model_type == 'MaskablePPO' and
+                                                               is_masking_supported(self.eval_env)))
 
         actions = self.train_env.get_actions()
         self.tensorboard_callback = TensorboardCallback(verbose=1, actions=actions)