Merge 7cbc0ce80a into 0afd5a7385

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:
         """

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:
         """

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:
         """

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 gymnasium import spaces
-from gym.utils import seeding
+from gymnasium.utils import seeding
 from pandas import DataFrame
 
 
@@ -203,7 +203,7 @@ 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):

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
@@ -433,7 +433,6 @@ class BaseReinforcementLearningModel(IFreqaiModel):
 
 def make_env(MyRLEnv: Type[gym.Env], 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

freqtrade/freqai/prediction_models/ReinforcementLearner_multiproc.py

@@ -4,7 +4,7 @@ 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 VecMonitor
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
 from freqtrade.freqai.prediction_models.ReinforcementLearner import ReinforcementLearner
 from freqtrade.freqai.RL.BaseReinforcementLearningModel import make_env
@@ -41,22 +41,27 @@ 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,
+        self.train_env = VecMonitor(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)])
+                                        in range(self.max_threads)]))
 
         eval_env_id = 'eval_env'
-        self.eval_env = SubprocVecEnv([make_env(self.MyRLEnv, eval_env_id, i, 1,
+        self.eval_env = VecMonitor(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)])
+                                       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)

requirements-freqai-rl.txt

@@ -3,8 +3,10 @@
 
 # Required for freqai-rl
 torch==1.13.1; python_version < '3.11'
-stable-baselines3==1.7.0; python_version < '3.11'
+#until these branches will be released we can use this
-sb3-contrib==1.7.0; python_version < '3.11'
+git+https://github.com/Farama-Foundation/Gymnasium@main
+git+https://github.com/DLR-RM/stable-baselines3@feat/gymnasium-support
+git+https://github.com/Stable-Baselines-Team/stable-baselines3-contrib@feat/gymnasium-support
 # 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'
+