style: black format

src/gymnasium_2048/agents/ntuple/factory.py

@@ -35,14 +35,12 @@ def get_all_rectangles_tuples(state: np.ndarray) -> Sequence[Sequence[int]]:
     # square
     for row in range(state.shape[0] - 1):
         for col in range(state.shape[1] - 1):
-            tuples.append(
-                (
-                    state[row, col],
-                    state[row, col + 1],
-                    state[row + 1, col + 1],
-                    state[row + 1, col],
-                )
-            )
+            tuples.append((
+                state[row, col],
+                state[row, col + 1],
+                state[row + 1, col + 1],
+                state[row + 1, col],
+            ))
 
     return tuples
 

src/gymnasium_2048/agents/ntuple/policy.py

@@ -96,9 +96,9 @@ def predict(self, state: np.ndarray) -> int:
         :param state: The board state.
         :return: Next action to play.
         """
-        return np.argmax([
-            self.evaluate(state=state, action=action) for action in range(4)
-        ])
+        return np.argmax(
+            [self.evaluate(state=state, action=action) for action in range(4)]
+        )
 
     @abstractmethod
     def save(self, path: str | pathlib.Path | io.BufferedIOBase) -> None:
@@ -207,9 +207,9 @@ def learn(
         after_state_tuples = self._get_tuples(state=after_state)
         after_state_value = self.net.predict(tuples=after_state_tuples)
 
-        next_action = np.argmax([
-            self.evaluate(state=next_state, action=a) for a in range(4)
-        ])
+        next_action = np.argmax(
+            [self.evaluate(state=next_state, action=a) for a in range(4)]
+        )
         next_after_state, next_reward, is_legal = TwentyFortyEightEnv.apply_action(
             board=next_state,
             action=next_action,