Assistance with the creation for this python language code. Please and thank you.

write an adversarial agent in the provided AlphaBetaAgent class in multiAgents.py to more efficiently explore the minimax tree. Your agent should work with any number of ghosts, so your algorithm should be a generalized version of the standard Alpha-Beta Pruning algorithm. The AlphaBetaAgent minimax values should be identical to the MinimaxAgent minimax values, although the actions it selects can vary because of different tie-breaking behavior. Note: The correct implementation of alpha-beta pruning will lead to Pacman losing some of the tests. This is not a problem: as it is correct behavior, it will pass the tests. Evaluation: Your code will be checked to determine whether it explores the correct number of game states. Therefore, it is important that you perform alpha-beta pruning without reordering children. In other words, successor states should always be processed in the order returned by GameState.getLegalActions. Again, do not call GameState.generateSuccessor more than necessary. Additionally, in order to match the set of states explored by the autograder, you must not prune on equality: that is, stop generating children for a max (min) node only if a child's value is strictly greater than (less than) (). To test and debug your code, run python autograder.py q q 3 This will show what your algorithm does on a number of small trees, as well as a pacman game. To run it without graphics, use: python autograder.py -q q3 --no-graphics write an adversarial agent in the provided AlphaBetaAgent class in multiAgents.py to more efficiently explore the minimax tree. Your agent should work with any number of ghosts, so your algorithm should be a generalized version of the standard Alpha-Beta Pruning algorithm. The AlphaBetaAgent minimax values should be identical to the MinimaxAgent minimax values, although the actions it selects can vary because of different tie-breaking behavior. Note: The correct implementation of alpha-beta pruning will lead to Pacman losing some of the tests. This is not a problem: as it is correct behavior, it will pass the tests. Evaluation: Your code will be checked to determine whether it explores the correct number of game states. Therefore, it is important that you perform alpha-beta pruning without reordering children. In other words, successor states should always be processed in the order returned by GameState.getLegalActions. Again, do not call GameState.generateSuccessor more than necessary. Additionally, in order to match the set of states explored by the autograder, you must not prune on equality: that is, stop generating children for a max (min) node only if a child's value is strictly greater than (less than) (). To test and debug your code, run python autograder.py q q 3 This will show what your algorithm does on a number of small trees, as well as a pacman game. To run it without graphics, use: python autograder.py -q q3 --no-graphics