add documentation for tensorboard_log, change how users interact with tensorboard_log

freqtrade/freqai/RL/Base4ActionRLEnv.py

@@ -48,7 +48,7 @@ class Base4ActionRLEnv(BaseEnvironment):
         self._update_unrealized_total_profit()
         step_reward = self.calculate_reward(action)
         self.total_reward += step_reward
-        self.tensorboard_metrics[self.actions._member_names_[action]] += 1
+        self.tensorboard_log(self.actions._member_names_[action])
 
         trade_type = None
         if self.is_tradesignal(action):

freqtrade/freqai/RL/Base5ActionRLEnv.py

@@ -49,7 +49,7 @@ class Base5ActionRLEnv(BaseEnvironment):
         self._update_unrealized_total_profit()
         step_reward = self.calculate_reward(action)
         self.total_reward += step_reward
-        self.tensorboard_metrics[self.actions._member_names_[action]] += 1
+        self.tensorboard_log(self.actions._member_names_[action])
 
         trade_type = None
         if self.is_tradesignal(action):

freqtrade/freqai/RL/BaseEnvironment.py

@@ -2,7 +2,7 @@ import logging
 import random
 from abc import abstractmethod
 from enum import Enum
-from typing import Optional, Type
+from typing import Optional, Type, Union
 
 import gym
 import numpy as np
@@ -132,14 +132,37 @@ class BaseEnvironment(gym.Env):
         self.np_random, seed = seeding.np_random(seed)
         return [seed]
 
+    def tensorboard_log(self, metric: str, inc: Union[int, float] = 1):
+        """
+        Function builds the tensorboard_metrics dictionary
+        to be parsed by the TensorboardCallback. This
+        function is designed for tracking incremented objects,
+        events, actions inside the training environment.
+        For example, a user can call this to track the
+        frequency of occurence of an `is_valid` call in
+        their `calculate_reward()`:
+
+        def calculate_reward(self, action: int) -> float:
+            if not self._is_valid(action):
+                self.tensorboard_log("is_valid")
+                return -2
+
+        :param metric: metric to be tracked and incremented
+        :param inc: value to increment `metric` by
+        """
+        if metric not in self.tensorboard_metrics:
+            self.tensorboard_metrics[metric] = inc
+        else:
+            self.tensorboard_metrics[metric] += inc
+
+    def reset_tensorboard_log(self):
+        self.tensorboard_metrics = {}
+
     def reset(self):
         """
         Reset is called at the beginning of every episode
         """
-        # tensorboard_metrics is used for episodic reports and tensorboard logging
-        self.tensorboard_metrics: dict = {}
-        for action in self.actions:
-            self.tensorboard_metrics[action.name] = 0
+        self.reset_tensorboard_log()
 
         self._done = False
 

freqtrade/freqai/prediction_models/ReinforcementLearner.py

@@ -100,6 +100,7 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
             """
             # first, penalize if the action is not valid
             if not self._is_valid(action):
+                self.tensorboard_log("is_valid")
                 return -2
 
             pnl = self.get_unrealized_profit()