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 pretrain(model):
feature_batch = []
targets_batch = []
board_positions = get_board_position()
shuffle(board_positions)
print("Pretraining on {} board positions...".format(len(board_positions)))
stockfish = Stockfish()
for batch in range(Config.PRETRAIN_EPOCHS):
for index, board_position in enumerate(board_positions):
if (index + 1) % Config.minibatch_size != 0:
feature_batch.append(board_to_feature(board_position))
targets_batch.append(
stockfish.stockfish_eval(board_position, 10))
else:
feature_batch = torch.FloatTensor(feature_batch)
targets_batch = Variable(torch.FloatTensor(targets_batch))
do_backprop(feature_batch, targets_batch, model)
feature_batch = []
targets_batch = []
print("Completed batch {} of {}".format(batch, Config.PRETRAIN_EPOCHS))