def value_policy(board: chess.Board):
env = ChessEnv(board)
game_over, score = env.is_game_over()
if game_over:
return score, []
stockfish = Stockfish()
value = stockfish.stockfish_eval(env.board, timeout=100)
next_states = []
for move in env.board.legal_moves:
board_copy = env.board.copy()
board_copy.push(move)
next_states.append(board_copy)
actions_value = []
for state in next_states:
actions_value.append(evaluate_state(state))
policy = softmax(actions_value)
index_list = [Config.MOVETOINDEX[move.uci()] for move in env.board.legal_moves]
map = np.zeros((5120,))
for index, pi in zip(index_list, policy):
map[index] = pi
assert policy.sum() > 0.999
return value, map
def evaluate_state(board):
# print(fen)
env = ChessEnv(board=board)
# env.step(move)
game_over, score = env.is_game_over()
if game_over:
return score
value = env.stockfish.stockfish_eval(env.board, timeout=100)
return value