reorganize/generalize tensorboard callback

freqtrade/freqai/prediction_models/ReinforcementLearner.py

@@ -88,33 +88,6 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
         User can override any function in BaseRLEnv and gym.Env. Here the user
         sets a custom reward based on profit and trade duration.
         """
-        def reset(self):
-
-            # Reset custom info
-            self.custom_info = {}
-            self.custom_info["Invalid"] = 0
-            self.custom_info["Hold"] = 0
-            self.custom_info["Unknown"] = 0
-            self.custom_info["pnl_factor"] = 0
-            self.custom_info["duration_factor"] = 0
-            self.custom_info["reward_exit"] = 0
-            self.custom_info["reward_hold"] = 0
-            for action in Actions:
-                self.custom_info[f"{action.name}"] = 0
-            return super().reset()
-
-        def step(self, action: int):
-            observation, step_reward, done, info = super().step(action)
-            info = dict(
-                tick=self._current_tick,
-                action=action,
-                total_reward=self.total_reward,
-                total_profit=self._total_profit,
-                position=self._position.value,
-                trade_duration=self.get_trade_duration(),
-                current_profit_pct=self.get_unrealized_profit()
-            )
-            return observation, step_reward, done, info
 
         def calculate_reward(self, action: int) -> float:
             """

freqtrade/freqai/prediction_models/ReinforcementLearner_multiproc.py

@@ -1,14 +1,14 @@
 import logging
-from typing import Any, Dict  # , Tuple
+from typing import Any, Dict
 
-# import numpy.typing as npt
 from pandas import DataFrame
 from stable_baselines3.common.callbacks import EvalCallback
 from stable_baselines3.common.vec_env import SubprocVecEnv
 
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
 from freqtrade.freqai.prediction_models.ReinforcementLearner import ReinforcementLearner
-from freqtrade.freqai.RL.BaseReinforcementLearningModel import TensorboardCallback, make_env
+from freqtrade.freqai.RL.BaseReinforcementLearningModel import make_env
+from freqtrade.freqai.RL.TensorboardCallback import TensorboardCallback
 
 
 logger = logging.getLogger(__name__)
@@ -50,4 +50,5 @@ class ReinforcementLearner_multiproc(ReinforcementLearner):
                                           render=False, eval_freq=len(train_df),
                                           best_model_save_path=str(dk.data_path))
 
-        self.tensorboard_callback = TensorboardCallback()
+        actions = self.train_env.env_method("get_actions")[0]
+        self.tensorboard_callback = TensorboardCallback(verbose=1, actions=actions)