def __init__(self,
game_state: GameState,
parent: 'MCTSNode' = None,
move: Move = None):
self.parent = parent
self.game_state = game_state
self.move = move
self.win_counts = {Player.white: 0, Player.black: 0}
self.num_rollouts = 0
self.children: List[MCTSNode] = []
self.univisted_moves = game_state.legal_moves()
def __init__(self, game_state: GameState, parent: MCTSNode = None, last_move: Move = None):
self.game_state = game_state
self.parent = parent
self.last_move = last_move
self.win_counts: Dict[Player, int] = {
Player.black: 0,
Player.white: 0,
}
self.num_rollouts: int = 0
self.children: List[MCTSNode] = []
b: Board = game_state.board
p: Player = game_state.next_player
moves: List[Move] = game_state.legal_moves()
self.unvisited_moves: List[Move] = [m for m in moves if not (m.point and is_point_an_eye(b, m.point, p))]
def select_move(self, game_state: GameState):
best_moves: List[Move] = []
best_score = float("-inf")
# Loop over all legal moves.
for possible_move in game_state.legal_moves():
if possible_move.point and is_point_an_eye(game_state.board, possible_move.point, game_state.next_player):
continue # skip the eye
next_state = game_state.apply_move(possible_move)
result = alphabeta(next_state, False, game_state.next_player, self.max_depth, ev_fn=self.ev_fn)
if (not best_moves) or result > best_score:
best_moves = [possible_move]
best_score = result
elif result == best_score:
best_moves.append(possible_move)
# For variety, randomly select among all equally good moves.
return random.choice(best_moves)