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