diff --git a/freqtrade/freqai/RL/BaseEnvironment.py b/freqtrade/freqai/RL/BaseEnvironment.py
index a31ded0c6..71b423844 100644
--- a/freqtrade/freqai/RL/BaseEnvironment.py
+++ b/freqtrade/freqai/RL/BaseEnvironment.py
@@ -137,15 +137,9 @@ class BaseEnvironment(gym.Env):
         Reset is called at the beginning of every episode
         """
         # custom_info is used for episodic reports and tensorboard logging
-        self.custom_info["Invalid"] = 0
-        self.custom_info["Hold"] = 0
-        self.custom_info["Unknown"] = 0
-        self.custom_info["pnl_factor"] = 0
-        self.custom_info["duration_factor"] = 0
-        self.custom_info["reward_exit"] = 0
-        self.custom_info["reward_hold"] = 0
+        self.custom_info: dict = {}
         for action in self.actions:
-            self.custom_info[f"{action.name}"] = 0
+            self.custom_info[action.name] = 0
 
         self._done = False
 
diff --git a/freqtrade/freqai/RL/TensorboardCallback.py b/freqtrade/freqai/RL/TensorboardCallback.py
index f590bdf84..d03c040d4 100644
--- a/freqtrade/freqai/RL/TensorboardCallback.py
+++ b/freqtrade/freqai/RL/TensorboardCallback.py
@@ -42,19 +42,18 @@ class TensorboardCallback(BaseCallback):
         )
 
     def _on_step(self) -> bool:
+
+        local_info = self.locals["infos"][0]
         custom_info = self.training_env.get_attr("custom_info")[0]
-        self.logger.record("_state/position", self.locals["infos"][0]["position"])
-        self.logger.record("_state/trade_duration", self.locals["infos"][0]["trade_duration"])
-        self.logger.record("_state/current_profit_pct", self.locals["infos"]
-                           [0]["current_profit_pct"])
-        self.logger.record("_reward/total_profit", self.locals["infos"][0]["total_profit"])
-        self.logger.record("_reward/total_reward", self.locals["infos"][0]["total_reward"])
-        self.logger.record_mean("_reward/mean_trade_duration", self.locals["infos"]
-                                [0]["trade_duration"])
-        self.logger.record("_actions/action", self.locals["infos"][0]["action"])
-        self.logger.record("_actions/_Invalid", custom_info["Invalid"])
-        self.logger.record("_actions/_Unknown", custom_info["Unknown"])
-        self.logger.record("_actions/Hold", custom_info["Hold"])
-        for action in self.actions:
-            self.logger.record(f"_actions/{action.name}", custom_info[action.name])
+
+        for info in local_info:
+            if info not in ["episode", "terminal_observation"]:
+                self.logger.record(f"_info/{info}", local_info[info])
+
+        for info in custom_info:
+            if info in [action.name for action in self.actions]:
+                self.logger.record(f"_actions/{info}", custom_info[info])
+            else:
+                self.logger.record(f"_custom/{info}", custom_info[info])
+
         return True
diff --git a/freqtrade/freqai/prediction_models/ReinforcementLearner.py b/freqtrade/freqai/prediction_models/ReinforcementLearner.py
index 47dbaf99e..1383ad15e 100644
--- a/freqtrade/freqai/prediction_models/ReinforcementLearner.py
+++ b/freqtrade/freqai/prediction_models/ReinforcementLearner.py
@@ -100,7 +100,6 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
             """
             # first, penalize if the action is not valid
             if not self._is_valid(action):
-                self.custom_info["Invalid"] += 1
                 return -2
 
             pnl = self.get_unrealized_profit()
@@ -109,15 +108,15 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
             # reward agent for entering trades
             if (action == Actions.Long_enter.value
                     and self._position == Positions.Neutral):
-                self.custom_info[f"{Actions.Long_enter.name}"] += 1
+                self.custom_info[Actions.Long_enter.name] += 1
                 return 25
             if (action == Actions.Short_enter.value
                     and self._position == Positions.Neutral):
-                self.custom_info[f"{Actions.Short_enter.name}"] += 1
+                self.custom_info[Actions.Short_enter.name] += 1
                 return 25
             # discourage agent from not entering trades
             if action == Actions.Neutral.value and self._position == Positions.Neutral:
-                self.custom_info[f"{Actions.Neutral.name}"] += 1
+                self.custom_info[Actions.Neutral.name] += 1
                 return -1
 
             max_trade_duration = self.rl_config.get('max_trade_duration_candles', 300)
@@ -131,22 +130,21 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
             # discourage sitting in position
             if (self._position in (Positions.Short, Positions.Long) and
                     action == Actions.Neutral.value):
-                self.custom_info["Hold"] += 1
+                self.custom_info[Actions.Neutral.name] += 1
                 return -1 * trade_duration / max_trade_duration
 
             # close long
             if action == Actions.Long_exit.value and self._position == Positions.Long:
                 if pnl > self.profit_aim * self.rr:
                     factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
-                self.custom_info[f"{Actions.Long_exit.name}"] += 1
+                self.custom_info[Actions.Long_exit.name] += 1
                 return float(pnl * factor)
 
             # close short
             if action == Actions.Short_exit.value and self._position == Positions.Short:
                 if pnl > self.profit_aim * self.rr:
                     factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
-                self.custom_info[f"{Actions.Short_exit.name}"] += 1
+                self.custom_info[Actions.Short_exit.name] += 1
                 return float(pnl * factor)
 
-            self.custom_info["Unknown"] += 1
             return 0.