improve typing, improve docstrings, ensure global tests pass

freqtrade/freqai/prediction_models/ReinforcementLearner.py

@@ -19,7 +19,15 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
     """
 
     def fit(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen, **kwargs):
-
+        """
+        User customizable fit method
+        :params:
+        data_dictionary: dict = common data dictionary containing all train/test
+            features/labels/weights.
+        dk: FreqaiDatakitchen = data kitchen for current pair.
+        :returns:
+        model: Any = trained model to be used for inference in dry/live/backtesting
+        """
         train_df = data_dictionary["train_features"]
         total_timesteps = self.freqai_info["rl_config"]["train_cycles"] * len(train_df)
 
@@ -59,7 +67,15 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
         """
 
         def calculate_reward(self, action):
-
+            """
+            An example reward function. This is the one function that users will likely
+            wish to inject their own creativity into.
+            :params:
+            action: int = The action made by the agent for the current candle.
+            :returns:
+            float = the reward to give to the agent for current step (used for optimization
+                of weights in NN)
+            """
             # first, penalize if the action is not valid
             if not self._is_valid(action):
                 return -2