class MCTSTrainer:
def __init__(self, gnn, test_graphs, filename):
self.mcts = MCTS(gnn)
self.test_graphs = test_graphs
self.test_result = []
self.filename = filename
# rollout until the end
def train1(self, graph, TAU, batch_size=10, iter_p=2):
self.mcts.train(graph, TAU, batch_size=batch_size, iter_p=iter_p)
# rollout only until leaf
def train2(self, graph, TAU, batch_size=10, iter_p=2):
self.mcts.train(graph,
TAU,
batch_size=batch_size,
stop_at_leaf=True,
iter_p=2)
def test(self):
result = [self.mcts.search(graph) for graph in self.test_graphs]
print(result)
self.test_result.append(result)
def save_test_result(self):
os.makedirs("log", exist_ok=True)
with open("log/{}.pickle".format(self.filename), mode="wb") as f:
pickle.dump(self.test_result, f)
def save_model(self):
os.makedirs("model", exist_ok=True)
torch.save(self.mcts.gnn.state_dict(),
"model/{}.pth".format(self.filename))
timeout = int(args.timeout) - 5
c1 = Client(args.ip, PORTS[player_arg], player_arg)
c1.send_name(PLAYER_NAMES[player_arg])
board = Board()
game = Game(board)
# Main game loop
try:
while not game.ended:
state = None
while state is None:
state, turn = c1.receive_state()
game.board.board = state
if turn not in ["black", "white"]:
raise GameEndedException
game.turn = TURN_MAPPING[turn]
print(state, turn)
if game.turn == OUR_PLAYER:
mcts = MCTS(deepcopy(game),
OUR_PLAYER,
max_depth=max_depth,
C=C)
start, end = mcts.search(timeout)
print(start, end)
c1.send_move(start, end)
except GameEndedException:
print("Game ended with state {}".format(turn))
c1.close()
