From f99dc9481aeb26d6120701034c35295d8134697e Mon Sep 17 00:00:00 2001
From: Quentin18 <quentindeschamps18@gmail.com>
Date: Fri, 26 Jan 2024 23:33:12 +0100
Subject: [PATCH] style: black format

---
 src/gymnasium_2048/agents/ntuple/factory.py | 14 ++++++--------
 src/gymnasium_2048/agents/ntuple/policy.py  | 12 ++++++------
 2 files changed, 12 insertions(+), 14 deletions(-)

diff --git a/src/gymnasium_2048/agents/ntuple/factory.py b/src/gymnasium_2048/agents/ntuple/factory.py
index b211fe5..d288ecc 100644
--- a/src/gymnasium_2048/agents/ntuple/factory.py
+++ b/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
 
diff --git a/src/gymnasium_2048/agents/ntuple/policy.py b/src/gymnasium_2048/agents/ntuple/policy.py
index c5cafc1..5eac8c9 100644
--- a/src/gymnasium_2048/agents/ntuple/policy.py
+++ b/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,