add continual retraining feature, handly mypy typing reqs, improve docstrings

freqtrade/freqai/prediction_models/ReinforcementLearner.py

@@ -1,7 +1,6 @@
 import logging
-from typing import Any, Dict  # , Tuple
+from typing import Any, Dict
 
-# import numpy.typing as npt
 import torch as th
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
 from freqtrade.freqai.RL.Base5ActionRLEnv import Actions, Base5ActionRLEnv, Positions
@@ -22,12 +21,18 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
         total_timesteps = self.freqai_info["rl_config"]["train_cycles"] * len(train_df)
 
         policy_kwargs = dict(activation_fn=th.nn.ReLU,
-                             net_arch=[256, 256, 128])
+                             net_arch=[512, 512, 256])
 
-        model = self.MODELCLASS(self.policy_type, self.train_env, policy_kwargs=policy_kwargs,
-                                tensorboard_log=Path(dk.data_path / "tensorboard"),
-                                **self.freqai_info['model_training_parameters']
-                                )
+        if dk.pair not in self.dd.model_dictionary or not self.continual_retraining:
+            model = self.MODELCLASS(self.policy_type, self.train_env, policy_kwargs=policy_kwargs,
+                                    tensorboard_log=Path(dk.data_path / "tensorboard"),
+                                    **self.freqai_info['model_training_parameters']
+                                    )
+        else:
+            logger.info('Continual training activated - starting training from previously '
+                        'trained agent.')
+            model = self.dd.model_dictionary[dk.pair]
+            model.set_env(self.train_env)
 
         model.learn(
             total_timesteps=int(total_timesteps),