diff --git a/freqtrade/freqai/RL/Base3ActionRLEnv.py b/freqtrade/freqai/RL/Base3ActionRLEnv.py
index c0a7eedaa..538ca3a6a 100644
--- a/freqtrade/freqai/RL/Base3ActionRLEnv.py
+++ b/freqtrade/freqai/RL/Base3ActionRLEnv.py
@@ -1,7 +1,7 @@
 import logging
 from enum import Enum
 
-from gym import spaces
+from gymnasium import spaces
 
 from freqtrade.freqai.RL.BaseEnvironment import BaseEnvironment, Positions
 
@@ -94,9 +94,12 @@ class Base3ActionRLEnv(BaseEnvironment):
 
         observation = self._get_observation()
 
+        # user can play with time if they want
+        truncated = False
+
         self._update_history(info)
 
-        return observation, step_reward, self._done, info
+        return observation, step_reward, self._done, truncated, info
 
     def is_tradesignal(self, action: int) -> bool:
         """
diff --git a/freqtrade/freqai/RL/Base4ActionRLEnv.py b/freqtrade/freqai/RL/Base4ActionRLEnv.py
index e883136b2..12f10d4fc 100644
--- a/freqtrade/freqai/RL/Base4ActionRLEnv.py
+++ b/freqtrade/freqai/RL/Base4ActionRLEnv.py
@@ -1,7 +1,7 @@
 import logging
 from enum import Enum
 
-from gym import spaces
+from gymnasium import spaces
 
 from freqtrade.freqai.RL.BaseEnvironment import BaseEnvironment, Positions
 
@@ -96,9 +96,12 @@ class Base4ActionRLEnv(BaseEnvironment):
 
         observation = self._get_observation()
 
+        # user can play with time if they want
+        truncated = False
+
         self._update_history(info)
 
-        return observation, step_reward, self._done, info
+        return observation, step_reward, self._done, truncated, info
 
     def is_tradesignal(self, action: int) -> bool:
         """
diff --git a/freqtrade/freqai/RL/Base5ActionRLEnv.py b/freqtrade/freqai/RL/Base5ActionRLEnv.py
index 816211cc2..35d04f942 100644
--- a/freqtrade/freqai/RL/Base5ActionRLEnv.py
+++ b/freqtrade/freqai/RL/Base5ActionRLEnv.py
@@ -1,7 +1,7 @@
 import logging
 from enum import Enum
 
-from gym import spaces
+from gymnasium import spaces
 
 from freqtrade.freqai.RL.BaseEnvironment import BaseEnvironment, Positions
 
@@ -101,10 +101,12 @@ class Base5ActionRLEnv(BaseEnvironment):
         )
 
         observation = self._get_observation()
+        # user can play with time if they want
+        truncated = False
 
         self._update_history(info)
 
-        return observation, step_reward, self._done, info
+        return observation, step_reward, self._done, truncated, info
 
     def is_tradesignal(self, action: int) -> bool:
         """
diff --git a/freqtrade/freqai/RL/BaseEnvironment.py b/freqtrade/freqai/RL/BaseEnvironment.py
index 7ac77361c..08bb93347 100644
--- a/freqtrade/freqai/RL/BaseEnvironment.py
+++ b/freqtrade/freqai/RL/BaseEnvironment.py
@@ -4,11 +4,11 @@ from abc import abstractmethod
 from enum import Enum
 from typing import Optional, Type, Union
 
-import gym
+import gymnasium as gym
 import numpy as np
 import pandas as pd
-from gym import spaces
-from gym.utils import seeding
+from gymnasium import spaces
+from gymnasium.utils import seeding
 from pandas import DataFrame
 
 
@@ -127,6 +127,14 @@ class BaseEnvironment(gym.Env):
         self.history: dict = {}
         self.trade_history: list = []
 
+    def get_attr(self, attr: str):
+        """
+        Returns the attribute of the environment
+        :param attr: attribute to return
+        :return: attribute
+        """
+        return getattr(self, attr)
+
     @abstractmethod
     def set_action_space(self):
         """
@@ -203,7 +211,7 @@ class BaseEnvironment(gym.Env):
         self.close_trade_profit = []
         self._total_unrealized_profit = 1
 
-        return self._get_observation()
+        return self._get_observation(), self.history
 
     @abstractmethod
     def step(self, action: int):
diff --git a/freqtrade/freqai/RL/BaseReinforcementLearningModel.py b/freqtrade/freqai/RL/BaseReinforcementLearningModel.py
index e10880f46..e2c0f5fda 100644
--- a/freqtrade/freqai/RL/BaseReinforcementLearningModel.py
+++ b/freqtrade/freqai/RL/BaseReinforcementLearningModel.py
@@ -6,7 +6,7 @@ from datetime import datetime, timezone
 from pathlib import Path
 from typing import Any, Callable, Dict, Optional, Tuple, Type, Union
 
-import gym
+import gymnasium as gym
 import numpy as np
 import numpy.typing as npt
 import pandas as pd
@@ -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']
@@ -431,9 +431,8 @@ 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,
-             monitor: bool = False,
              env_info: Dict[str, Any] = {}) -> Callable:
     """
     Utility function for multiprocessed env.
@@ -450,8 +449,7 @@ def make_env(MyRLEnv: Type[gym.Env], env_id: str, rank: int,
 
         env = MyRLEnv(df=train_df, prices=price, id=env_id, seed=seed + rank,
                       **env_info)
-        if monitor:
-            env = Monitor(env)
+
         return env
     set_random_seed(seed)
     return _init
diff --git a/freqtrade/freqai/RL/TensorboardCallback.py b/freqtrade/freqai/RL/TensorboardCallback.py
index 7f8c76956..61652c9c6 100644
--- a/freqtrade/freqai/RL/TensorboardCallback.py
+++ b/freqtrade/freqai/RL/TensorboardCallback.py
@@ -3,8 +3,9 @@ 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 VecEnv
 
-from freqtrade.freqai.RL.BaseEnvironment import BaseActions, BaseEnvironment
+from freqtrade.freqai.RL.BaseEnvironment import BaseActions
 
 
 class TensorboardCallback(BaseCallback):
@@ -12,11 +13,13 @@ class TensorboardCallback(BaseCallback):
     Custom callback for plotting additional values in tensorboard and
     episodic summary reports.
     """
+    # Override training_env type to fix type errors
+    training_env: Union[VecEnv, None] = None
+
     def __init__(self, verbose=1, actions: Type[Enum] = BaseActions):
         super().__init__(verbose)
         self.model: Any = None
-        self.logger = None  # type: Any
-        self.training_env: BaseEnvironment = None  # type: ignore
+        self.logger: Any = None
         self.actions: Type[Enum] = actions
 
     def _on_training_start(self) -> None:
@@ -44,6 +47,8 @@ class TensorboardCallback(BaseCallback):
     def _on_step(self) -> bool:
 
         local_info = self.locals["infos"][0]
+        if self.training_env is None:
+            return True
         tensorboard_metrics = self.training_env.get_attr("tensorboard_metrics")[0]
 
         for metric in local_info:
diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py
index 039b6a175..3580963d4 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)
 
diff --git a/freqtrade/freqai/prediction_models/ReinforcementLearner.py b/freqtrade/freqai/prediction_models/ReinforcementLearner.py
index 65990da87..a5c2e12b5 100644
--- a/freqtrade/freqai/prediction_models/ReinforcementLearner.py
+++ b/freqtrade/freqai/prediction_models/ReinforcementLearner.py
@@ -1,11 +1,12 @@
 import logging
 from pathlib import Path
-from typing import Any, Dict
+from typing import Any, Dict, Type
 
 import torch as th
 
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
 from freqtrade.freqai.RL.Base5ActionRLEnv import Actions, Base5ActionRLEnv, Positions
+from freqtrade.freqai.RL.BaseEnvironment import BaseEnvironment
 from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel
 
 
@@ -84,7 +85,9 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
 
         return model
 
-    class MyRLEnv(Base5ActionRLEnv):
+    MyRLEnv: Type[BaseEnvironment]
+
+    class MyRLEnv(Base5ActionRLEnv):  # type: ignore[no-redef]
         """
         User can override any function in BaseRLEnv and gym.Env. Here the user
         sets a custom reward based on profit and trade duration.
diff --git a/freqtrade/freqai/prediction_models/ReinforcementLearner_multiproc.py b/freqtrade/freqai/prediction_models/ReinforcementLearner_multiproc.py
index b3b8c40e6..73f617027 100644
--- a/freqtrade/freqai/prediction_models/ReinforcementLearner_multiproc.py
+++ b/freqtrade/freqai/prediction_models/ReinforcementLearner_multiproc.py
@@ -3,7 +3,7 @@ from typing import Any, Dict
 
 from pandas import DataFrame
 from stable_baselines3.common.callbacks import EvalCallback
-from stable_baselines3.common.vec_env import SubprocVecEnv
+from stable_baselines3.common.vec_env import SubprocVecEnv, VecMonitor
 
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
 from freqtrade.freqai.prediction_models.ReinforcementLearner import ReinforcementLearner
@@ -41,22 +41,25 @@ class ReinforcementLearner_multiproc(ReinforcementLearner):
 
         env_info = self.pack_env_dict(dk.pair)
 
+        eval_freq = len(train_df) // self.max_threads
+
         env_id = "train_env"
-        self.train_env = SubprocVecEnv([make_env(self.MyRLEnv, env_id, i, 1,
-                                        train_df, prices_train,
-                                        monitor=True,
-                                        env_info=env_info) for i
-                                        in range(self.max_threads)])
+        self.train_env = VecMonitor(SubprocVecEnv([make_env(self.MyRLEnv, env_id, i, 1,
+                                                            train_df, prices_train,
+                                                            env_info=env_info) for i
+                                                   in range(self.max_threads)]))
 
         eval_env_id = 'eval_env'
-        self.eval_env = SubprocVecEnv([make_env(self.MyRLEnv, eval_env_id, i, 1,
-                                                test_df, prices_test,
-                                                monitor=True,
-                                                env_info=env_info) for i
-                                       in range(self.max_threads)])
+        self.eval_env = VecMonitor(SubprocVecEnv([make_env(self.MyRLEnv, eval_env_id, i, 1,
+                                                           test_df, prices_test,
+                                                           env_info=env_info) for i
+                                                  in range(self.max_threads)]))
+
         self.eval_callback = EvalCallback(self.eval_env, deterministic=True,
-                                          render=False, eval_freq=len(train_df),
+                                          render=False, eval_freq=eval_freq,
                                           best_model_save_path=str(dk.data_path))
 
+        # TENSORBOARD CALLBACK DOES NOT RECOMMENDED TO USE WITH MULTIPLE ENVS,
+        # IT WILL RETURN FALSE INFORMATIONS, NEVERTHLESS NOT THREAD SAFE WITH SB3!!!
         actions = self.train_env.env_method("get_actions")[0]
         self.tensorboard_callback = TensorboardCallback(verbose=1, actions=actions)
diff --git a/requirements-freqai-rl.txt b/requirements-freqai-rl.txt
index f4e1e557b..45ccc40cc 100644
--- a/requirements-freqai-rl.txt
+++ b/requirements-freqai-rl.txt
@@ -3,10 +3,11 @@
 
 # Required for freqai-rl
 torch==1.13.1; python_version < '3.11'
-stable-baselines3==1.7.0; python_version < '3.11'
-sb3-contrib==1.7.0; python_version < '3.11'
+#until these branches will be released we can use this
+gymnasium==0.28.1
+stable_baselines3==2.0.0a5
+sb3_contrib>=2.0.0a4
 # Gym is forced to this version by stable-baselines3.
 setuptools==65.5.1 # Should be removed when gym is fixed.
-gym==0.21; python_version < '3.11'
 # Progress bar for stable-baselines3 and sb3-contrib
 tqdm==4.65.0; python_version < '3.11'