def main():
training_size = 1000
index = range(training_size)
epochs = 1000
one_epoch_word_num = training_size * 3
giver = PGACAgent(player_tag='giver', is_giver=True, restrict_value=False)
all_words = giver.get_all_targets()
words = itemgetter(*index)(all_words)
words = [w[0] for w in words]
res_dir = "./single_pg_res"
if not os.path.exists(res_dir):
os.mkdir(res_dir)
path = 'single_pg_{0}.json'.format(CURRENT_TIME)
path = os.path.join(res_dir, path)
to_save_path = os.path.join(res_dir, 'model_{}'.format(CURRENT_TIME))
for epoch in range(epochs):
print("Current Epoch {}".format(epoch))
for guesser in deterministic_guessers:
print("In the testing set")
if epoch:
test_on_words(words, giver, guesser, epoch=epoch, path=path)
for num in range(one_epoch_word_num):
guesser = random.choice(deterministic_guessers)
add_experience(giver, guesser, words)
try:
if num % 100 == 99:
print("Finished a training process")
giver.train()
except:
import pdb
pdb.set_trace()
giver.model.save_model(epoch=epoch, path=to_save_path)
print('=======================')
def main():
training_size = 1000
index, test_index = range(training_size), range(training_size,
training_size + 59)
epochs = 1000
one_epoch_word_num = training_size * 3
giver = PGACAgent(player_tag='giver', is_giver=True, restrict_value=False)
all_words = giver.get_all_targets()
words = itemgetter(*index)(all_words)
words = [w[0] for w in words]
test_words = itemgetter(*test_index)(all_words)
test_words = [w[0] for w in test_words]
for epoch in range(epochs):
print("Current Epoch {}".format(epoch))
for guesser in deterministic_guessers:
print("In the testing set")
if epoch:
test_on_words(test_words, giver, guesser)
for num in range(one_epoch_word_num):
guesser = random.choice(deterministic_guessers)
add_experience(giver, guesser, words)
try:
if num % 100 == 99:
giver.train()
except:
continue
print('=======================')
def main():
training_size = 1000
giver = PGACAgent(player_tag='giver',
is_giver=True,
restrict_value=False)
guesser = PGACAgent(player_tag='guesser',
is_giver=False, FSG=True,
restrict_value=False,
REV_FSG_NORM=True)
epochs = 1000
index = list(range(training_size))
one_epoch_word_num = training_size * 3
all_words = giver.get_all_targets()
words = itemgetter(*index)(all_words)
words = [w[0] for w in words]
deterministic_guessers.append(guesser)
path = './double_pg_{}.json'.format(CURRENT_TIME)
for epoch in range(epochs):
print("Current Epoch {}".format(epoch))
if epoch:
for to_test_guesser in deterministic_guessers:
test_on_words(words, giver, to_test_guesser, epoch=epoch, path=path)
for num in range(one_epoch_word_num):
add_experience(giver, guesser, words)
if num % 100 == 99:
print("Trained on a batch")
giver.train()
guesser.train()
print('=======================')
def main():
giver = ActorCriticAgent(player_tag='giver',
is_giver=True,
restrict_value=True)
training_size = 1000
epochs = 1000
one_epoch_word_num = training_size * 3
all_words = giver.get_all_targets()
words = all_words[:training_size]
words = [w[0] for w in words]
res_dir = "./single_naive_res"
if not os.path.exists(res_dir):
os.mkdir(res_dir)
path = 'single_naive_{0}.json'.format(CURRENT_TIME)
path = os.path.join(res_dir, path)
to_save_path = os.path.join(res_dir, 'model_{}'.format(CURRENT_TIME))
for epoch in range(epochs):
print("Current Epoch {}".format(epoch))
guesser = deterministic_guessers[0]
for guesser in deterministic_guessers:
# print("In the training set")
# test_on_words(words, giver, guesser)
print("In the testing set")
if epoch:
test_on_words(words, giver, guesser, epoch=epoch, path=path)
for num in range(one_epoch_word_num):
guesser = random.choice(deterministic_guessers)
#guesser = deterministic_guessers[0]
add_experience(giver, guesser, words)
if num % 100 == 99:
print("Trained on a batch")
giver.train()
if num == (one_epoch_word_num - 1):
giver.add_train_counter()
giver.model.save_model(epoch=epoch, path=to_save_path)
print('=======================')
def topic_guesses_checker():
giver = giver12
training_size = 200
all_words = giver.get_all_targets()
words = all_words[:training_size]
words = [w[0] for w in words]
print("Test for 1")
giver = PGACAgent(player_tag='giver', is_giver=True, restrict_value=False)
giver.model.load_model('./remote_models/single_pg/giver')
test_on_words(words, giver, guesser12, epoch=0, to_print_traj=False)
print("Test for Topic 1")
for guesser in guesser12_family:
test_on_words(words, giver, guesser, epoch=0, to_print_traj=False)
print("Test for 2")
test_on_words(words, giver, guesser22, epoch=0, to_print_traj=False)
print("Test for Topic 2")
for guesser in guesser22_family:
test_on_words(words, giver, guesser, epoch=0, to_print_traj=False)
print("Test for 3")
test_on_words(words, giver, guesser32, epoch=0, to_print_traj=False)
print("Test for Topic 3")
for guesser in guesser32_family:
test_on_words(words, giver, guesser, epoch=0, to_print_traj=False)
# testing
print("Test for 4")
test_on_words(words, giver, guesser42, epoch=0, to_print_traj=False)
print("Test for Topic 4")
for guesser in guesser42_family:
test_on_words(words, giver, guesser, epoch=0, to_print_traj=False)
print("Test for 5")
test_on_words(words, giver, guesser52, epoch=0, to_print_traj=False)
print("Test for Topic 5")
for guesser in guesser52_family:
test_on_words(words, giver, guesser, epoch=0, to_print_traj=False)
