automatically handle model_save_type for user

docs/freqai-parameter-table.md

@@ -53,7 +53,7 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
 | `max_trade_duration_candles`| Guides the agent training to keep trades below desired length. Example usage shown in `prediction_models/ReinforcementLearner.py` within the user customizable `calculate_reward()` <br> **Datatype:** int.
 | `model_type` | Model string from stable_baselines3 or SBcontrib. Available strings include: `'TRPO', 'ARS', 'RecurrentPPO', 'MaskablePPO', 'PPO', 'A2C', 'DQN'`. User should ensure that `model_training_parameters` match those available to the corresponding stable_baselines3 model by visiting their documentaiton. [PPO doc](https://stable-baselines3.readthedocs.io/en/master/modules/ppo.html) (external website) <br> **Datatype:** string.
 | `policy_type` | One of the available policy types from stable_baselines3 <br> **Datatype:** string.
-| `continual_learning` | If true, the agent will start new trainings from the model selected during the previous training. If false, a new agent is trained from scratch for each training. <br> **Datatype:** Bool.
+| `max_training_drawdown_pct` | The maximum drawdown that the agent is allowed to experience during training. <br> **Datatype:** float. <br> Default: 0.8
 | `cpu_count` | Number of threads/cpus to dedicate to the Reinforcement Learning training process (depending on if `ReinforcementLearning_multiproc` is selected or not). <br> **Datatype:** int. 
 | `model_reward_parameters` | Parameters used inside the user customizable `calculate_reward()` function in `ReinforcementLearner.py` <br> **Datatype:** int.
 |  |  **Extraneous parameters**

docs/freqai-reinforcement-learning.md

@@ -118,7 +118,6 @@ In order to configure the `Reinforcement Learner` the following dictionary to th
             "cpu_count": 8,
             "model_type": "PPO",
             "policy_type": "MlpPolicy",
-            "continual_learning": false,
             "model_reward_parameters": {
                 "rr": 1,
                 "profit_aim": 0.025

freqtrade/freqai/data_drawer.py

@@ -92,6 +92,12 @@ class FreqaiDataDrawer:
                 "model_filename": "", "trained_timestamp": 0,
                 "data_path": "", "extras": {}}
         self.limit_ram_use = self.freqai_info.get('limit_ram_usage', False)
+        if 'rl_config' in self.freqai_info:
+            self.model_type = 'stable_baselines'
+            logger.warning('User indicated rl_config, FreqAI will now use stable_baselines3'
+                           ' to save models.')
+        else:
+            self.model_type = self.freqai_info.get('model_save_type', 'joblib')
 
     def load_drawer_from_disk(self):
         """
@@ -414,12 +420,11 @@ class FreqaiDataDrawer:
         save_path = Path(dk.data_path)
 
         # Save the trained model
-        model_type = self.freqai_info.get('model_save_type', 'joblib')
+        if self.model_type == 'joblib':
-        if model_type == 'joblib':
             dump(model, save_path / f"{dk.model_filename}_model.joblib")
-        elif model_type == 'keras':
+        elif self.model_type == 'keras':
             model.save(save_path / f"{dk.model_filename}_model.h5")
-        elif 'stable_baselines' in model_type:
+        elif 'stable_baselines' in self.model_type:
             model.save(save_path / f"{dk.model_filename}_model.zip")
 
         if dk.svm_model is not None:
@@ -496,16 +501,15 @@ class FreqaiDataDrawer:
             dk.data_path / f"{dk.model_filename}_trained_df.pkl"
         )
 
-        model_type = self.freqai_info.get('model_save_type', 'joblib')
         # try to access model in memory instead of loading object from disk to save time
         if dk.live and coin in self.model_dictionary and not self.limit_ram_use:
             model = self.model_dictionary[coin]
-        elif model_type == 'joblib':
+        elif self.model_type == 'joblib':
             model = load(dk.data_path / f"{dk.model_filename}_model.joblib")
-        elif model_type == 'keras':
+        elif self.model_type == 'keras':
             from tensorflow import keras
             model = keras.models.load_model(dk.data_path / f"{dk.model_filename}_model.h5")
-        elif model_type == 'stable_baselines':
+        elif self.model_type == 'stable_baselines':
             mod = __import__('stable_baselines3', fromlist=[
                              self.freqai_info['rl_config']['model_type']])
             MODELCLASS = getattr(mod, self.freqai_info['rl_config']['model_type'])