def minimax_alpha_beta(moves_log,
state: game.GameState,
max_player: MinimaxPlayer,
max_depth=5,
depth=0,
alpha=-INF,
beta=INF) -> (int, int):
winner = state.get_winner()
if winner is not None:
# TODO: consider depth
score = INF - depth if winner is max_player else -INF + depth
return NO_MOVE, score # state.eval_state()
moves = game.to_non_empty(state.get_moves())
if not moves:
return NO_MOVE, TIE_SCORE
moves = list(moves)
random.shuffle(moves)
if depth >= max_depth:
return NO_MOVE, state.eval()
is_max_player = (depth % 2 == 0)
best_moves = []
best_score = None
for move in moves:
recursive_score = \
minimax_alpha_beta(moves_log + [move], state.move(move), max_player, max_depth, depth + 1, alpha, beta)[1]
if is_max_player:
if best_score is None or recursive_score > best_score:
best_score = recursive_score
best_moves = [move]
if best_score > alpha:
alpha = recursive_score
elif recursive_score == best_score:
best_moves.append(move)
else:
if best_score is None or recursive_score < best_score:
best_score = recursive_score
best_moves = [move]
if best_score < beta:
beta = recursive_score
elif recursive_score == best_score:
best_moves.append(move)
if alpha > beta:
break
# print(f'Depth {depth}/{max_depth}: [{",".join(str(i) for i in moves_log)}] | '
# f'As {state.get_curr_player().get_char()}, '
# f'my best moves are: [{",".join(str(i) for i in best_moves)}] with score: {best_score}')
return random.choice(best_moves) if best_moves else None, best_score
