def __init__(self, game_state, parent=None, move=None):
self.game_state = game_state
self.parent = parent
self.move = move
self.children = []
self.win_count = {Player.black: 0, Player.white: 0}
self.num_rollouts = 0
self.unvisited_moves = legal_moves(game_state) + [Move.pass_turn()]
def select_move(self, game_state):
best_moves = []
best_so_far = -MAX_SCORE
for move in legal_moves(game_state):
next_state = game_state.apply_move(move)
opponent_best = alpha_beta_best(next_state, best_so_far, self.max_depth, self.eval_fn)
# opponent_best = best_result(next_state, self.max_depth, self.eval_fn)
our_result = -opponent_best
if our_result > best_so_far:
best_moves = [move]
best_so_far = our_result
elif our_result == best_so_far:
best_moves.append(move)
if not best_moves:
return Move.pass_turn()
return random.choice(best_moves)
def best_result(state, depth, eval_fn):
if state.is_over():
if state.winner() == state.next_player:
return MAX_SCORE
else:
return -MAX_SCORE
elif depth == 0:
return eval_fn(state)
else:
best_so_far = -MAX_SCORE
for move in legal_moves(state):
next_state = state.apply_move(move)
opponent_best = best_result(next_state, depth-1, eval_fn)
our_best = - opponent_best
if our_best > best_so_far:
best_so_far = our_best
if best_so_far == MAX_SCORE:
return best_so_far
return best_so_far
def alpha_beta_best(state, alpha, depth, eval_fn):
if state.is_over():
if state.winner() == state.next_player:
return MAX_SCORE
else:
return -MAX_SCORE
elif depth == 0:
return eval_fn(state)
else:
best_so_far = -MAX_SCORE
for move in legal_moves(state):
next_state = state.apply_move(move)
opposite_result = alpha_beta_best(next_state, best_so_far, depth - 1, eval_fn)
if opposite_result < alpha:
return opposite_result
our_result = - opposite_result
if our_result > best_so_far:
best_so_far = our_result
return best_so_far
def select_move(self, game_state):
candidates = legal_moves(game_state)
if not candidates:
return Move.pass_turn()
return random.choice(candidates)