feat: 🚀 genetic agent class and it's training algorithm commenced

main.py

@@ -41,6 +41,7 @@ def test():
 
     # # manager.startGame()
 
+<<<<<<< HEAD
     # # train()
 
 
@@ -52,3 +53,7 @@ def test():
 
 
     # cProfile.run('main()', 'restats')  
+=======
+    #train()
+
+>>>>>>> c14418b (feat: :rocket: genetic agent class and it's training algorithm commenced)

src/agents/agent.py

@@ -47,6 +47,7 @@ def play_game(agent: Agent, board: Tetris, actions_per_drop: int = 1, max_pieces
     Returns:
         The final state of the board after the game is over.
     """
+<<<<<<< HEAD
 <<<<<<< HEAD
     #count = 0
 
@@ -55,6 +56,10 @@ def play_game(agent: Agent, board: Tetris, actions_per_drop: int = 1, max_pieces
     pieces_dropped = 0
     while not board.isGameOver() and pieces_dropped < max_pieces_dropped:
 >>>>>>> b842fc6 (feat: :rocket: genetic agent class and it's training algorithm commenced)
+=======
+    pieces_dropped = 0
+    while not board.isGameOver() and pieces_dropped < max_pieces_dropped:
+>>>>>>> c14418b (feat: :rocket: genetic agent class and it's training algorithm commenced)
         # Get the result of the agent's action
         for _ in range(actions_per_drop):
             result = agent.result(board)

src/agents/heuristic.py

@@ -81,8 +81,12 @@ def max_height(gameState: Tetris) -> int:
     return max(checkedList)
 
 
+<<<<<<< HEAD
 # Does this work? row cleared in get_possible_boards??
 def lines_cleaned(gameState: Tetris) -> int:
+=======
+def lines_cleared(gameState: Tetris) -> int:
+>>>>>>> c14418b (feat: :rocket: genetic agent class and it's training algorithm commenced)
     """Retrurns the number of lines cleared."""
     sum = 0
     for row in gameState.board:

src/agents/heuristic_agent.py

@@ -24,6 +24,16 @@ def result(self, board: Tetris) -> list[Action]:
 
         # Find the actions needed to transform the current board to the new board
         actions = []
+<<<<<<< HEAD
         actions = transition_model(board, best_board)
         return actions
 
+=======
+        try:
+            actions = transition_model(board, best_board)
+            return actions
+        except:
+            return actions
+
+
+>>>>>>> c14418b (feat: :rocket: genetic agent class and it's training algorithm commenced)