def get_user_move(em, color):
userInput = input("What's your move? (Type it like a2a3)")
lastInput = "xxxxx"
if userInput[0] == "a":
lastInput = lastInput[0] + "0" + lastInput[2:]
elif userInput[0] == "b":
lastInput = lastInput[0] + "1" + lastInput[2:]
elif userInput[0] == "c":
lastInput = lastInput[0] + "2" + lastInput[2:]
lastInput = str(6 - int(userInput[1])) + lastInput[1:]
lastInput = lastInput[:2] + str(6 - int(userInput[3])) + lastInput[4:]
if userInput[2] == "a":
lastInput = lastInput[:3] + "0" + lastInput[4:]
elif userInput[2] == "b":
lastInput = lastInput[:3] + "1" + lastInput[4:]
elif userInput[2] == "c":
lastInput = lastInput[:3] + "2" + lastInput[4:]
pieceNotationBeforeMove = em.board[int(lastInput[0])][int(lastInput[1])]
#No promotion
if pieceNotationBeforeMove[1] != "P" or (
pieceNotationBeforeMove == "+P"
and lastInput[0] != "1") or (pieceNotationBeforeMove == "-P"
and lastInput[0] != "4"):
lastInput = lastInput[:4] + pieceNotationBeforeMove[1]
#Promotion
else:
lastInput = lastInput[:4] + "=R"
incoming = mcts.State(em, color)
outgoing = incoming.next_state(0, False, lastInput)
return outgoing.BoardObject
def execute_generate_play(nnet_path,
multiplikator=configs.SIMS_FAKTOR,
exponent=configs.SIMS_EXPONENT):
gc.collect()
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
nnet = Network.get_net(configs.FILTERS, configs.HIDDEN_SIZE,
configs.OUT_FILTERS, configs.NUM_ACTIONS,
configs.INPUT_SIZE, None, configs.NUM_RESIDUAL)
nnet.load_weights(nnet_path)
env = MillEnv.MillEnv()
mcts_ = mcts.MonteCarloTreeSearch(
mcts.State(zeros((1, 24)), 0, -env.isPlaying, env))
stmem = mcts_.generatePlay(nnet, multiplikator, exponent)
del mcts_
del env
keras.backend.clear_session()
tf.compat.v1.reset_default_graph()
del nnet
gc.collect()
return stmem
def real_game(modelname, time_limit, recommendation_count):
mode = ask_question("What gamemode are you playing?", ["ap", "cm"])
side = ask_question("Which side are you playing on?", ["radiant", "dire"])
first = ask_question("Do you have first pick / ban?", ["y", "n"])
if mode == "ap":
util.pick_ban_order = util.allpick_order
else:
util.pick_ban_order = util.cm_order
radiant_goes_first = (side == "radiant"
and first == "y") or (side == "dire"
and first == "n")
node = mcts_transpositions.Node(mcts.State(radiant_goes_first))
transpositions = dict()
model = util.load_model(modelname)
players_turn = (side == "radiant" and node.state.radiant_moves_next) or (
side == "dire" and not node.state.radiant_moves_next)
while not node.state.is_terminal():
print_state(node.state)
choices = node.state.get_actions()
choices_sets = [set(i) for i in choices]
(pick_ban, count) = util.pick_ban_order[node.state.pick_ban_position]
subject = 'pick' if pick_ban == util.pick else 'ban'
print("The next action is a", subject, "of", count, "heroes.")
if players_turn:
print("It is your turn. MCTS recommends the following heroes: ...")
(_, root_node, transpositions) = mcts_transpositions.uct_search(
model,
initial_node=node,
time_limit=time_limit,
transpositions=transpositions)
node = root_node
def to_transpo(n):
return transpositions[mcts_transpositions.state_to_key(
n.state)]
children = sorted(
root_node.children,
key=lambda n: to_transpo(
n).total_simulated_reward / to_transpo(n).visit_count,
reverse=True)
for c in children[:recommendation_count]:
print([
util.simple_heroes.ordered_to_name(i)
for i in c.incoming_action
],
to_transpo(c).total_simulated_reward /
to_transpo(c).visit_count,
to_transpo(c).visit_count)
else:
print("It is the other team's turn. What did they do?")
players_turn = not players_turn
choice = get_pick(node.state, pick_ban, count)
print()
assert (set(choice) in choices_sets)
found = False
for n in node.children:
if n.incoming_action == choice:
node = n
node.parent = None
node.incoming_action = None
found = True
if not found:
node = mcts.Node(node.state.get_next_state(choice))
print('Done!')
print_state(node.state)
print('Predicting Radiant win probability with all models:')
for model_name in util.all_models:
model = util.load_model(model_name)
print(
model_name, ':',
util.predict_radiant_win_probability(
util.state_to_feature(node.state), model))
def setUp(self) -> None:
self.env = MillEnv.MillEnv()
import Network
self.nnet = Network.get_net(96, 3, 256, 4, 1, 24, (8, 3, 4))
self.mcts = mcts.MonteCarloTreeSearch(mcts.State(np.zeros((1, 24)), 0, -self.env.isPlaying, self.env))
def test_dirichlet(self):
self.env.makeMove(1)
self.state = mcts.State(np.zeros((1, 24)), 0, -self.env.isPlaying, self.env)
self.state.add_noise(np.random.default_rng())
self.assertNotEqual(self.state.priors[0, 0], 0)
def setUp(self) -> None:
import Network
self.env = MillEnv.MillEnv()
self.state = mcts.State(np.zeros((1, 24)), 0, -self.env.isPlaying, self.env)
self.nnet = Network.get_net(24, 3, 64, 4, 1, 24, (8, 3, 4))