def main():
n = 5
env = PermutationSorting(n)
st = RBFStateTransformer(env, MaxStateTransformer(n))
agent = TDLambdaAgent(env, st)
agent.train(500, Eps1().eps, True)
print(agent.solve([0, 4, 3, 1, 2]))
def main(argv):
np.random.seed(12345678)
n = int(argv[1])
n_neighbors = 0.1
pretrain = './saved_models/ddpg_tf_pretrain_weights_' + str(n) + str(
n_neighbors) + '.h5'
train = './saved_models/ddpg_tf_final_weights_' + str(n) + str(
n_neighbors) + '.h5'
env = PermutationSorting(n, transpositions=True)
state_transformer = OneHotStateTransformer(n)
actor_layer_sizes = [400, 300]
critic_layer_sizes = [400, 300]
agent = DDPGAgent(env,
state_transformer,
actor_layer_sizes,
critic_layer_sizes,
batch_size=32,
train_start=60000,
maxlen=1e6,
neighbors_percent=n_neighbors,
render=False,
fill_mem=True,
pretrain_path=pretrain,
train_path=train)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
agent.set_session(sess)
sess.run(tf.global_variables_initializer())
agent.train(episodes=1000)
def main():
np.random.seed(12345678)
n = 15
env = PermutationSorting(n, transpositions=True)
state_transformer = OneHotStateTransformer(n)
agent = DDQNAgent(env, state_transformer, batch_size=126)
#agent.serial_pretrain()
# agent.load_pretrain_weights()
# agent.load_final_weights()
agent.fill_memory()
agent.train()
def main(argv):
np.random.seed(12345678)
n = int(argv[1])
env = PermutationSorting(n, transpositions=True)
state_transformer = OneHotStateTransformer(n)
agent = DDPGAgent(env,
state_transformer,
batch_size=64,
neighbors_percent=0.1,
render=False)
agent.fill_memory()
agent.train()
def ddqn_10():
np.random.seed(12345678)
n = 10
env = PermutationSorting(n, transpositions=True)
state_transformer = OneHotStateTransformer(n)
ddqn = DDQNAgent(env,
state_transformer,
0,
hidden_layer_sizes=(400, 300),
batch_size=256,
train_start=60000,
epsilon=0.09,
render=False,
train_path='./saved_models/ddqn_tf_final_weights_' +
str(n) + '.h5')
path = "./data/10_exact/all10urt"
path_res = "./data/10_res/ddqn_exact"
with tf.Session() as sess:
ddqn.set_session(sess)
ddqn.load_weights()
n_better = 0
n_worse = 0
n_equal = 0
t_dif = 0
start = time.time()
with open(path_res, 'w') as fp:
for i in range(10000):
f = open(path, 'r')
line = random_line(f)
p = np.fromstring(line, dtype=int, sep=',')
p -= 1
res = re.search(r'\s+\d+', line)
exact_ans = int(res.group())
f.close()
ddqn_ans = ddqn.solve(p)
dif = float(ddqn_ans) / float(exact_ans)
t_dif = dif if t_dif == 0 else (dif + t_dif) / 2
if ddqn_ans < exact_ans: n_better += 1
elif ddqn_ans > exact_ans: n_worse += 1
else: n_equal += 1
string = str(i) + ' - ' + str(p) + ' - ' + ' DDQN: ' + str(ddqn_ans) + ' Exact: ' +\
str(exact_ans) + ' Difference: ' + str(dif)
print(time.time() - start, string)
fp.write(string + '\n')
string = "Total difference: " + str(t_dif) + " Times better: " + str(n_better) + " Times worse: " +\
str(n_worse) + " Times equal: " + str(n_equal)
print(string)
fp.write(string + '\n')
def main(argv): np.random.seed(12345678) n = int(argv[1]) pretrain = './saved_models/ddqn_tf_pretrain_weights_' + str(n) + '.h5' train = './saved_models/ddqn_tf_final_weights_' + str(n) + '.h5' env = PermutationSorting(n, transpositions=True) state_transformer = OneHotStateTransformer(n) hidden_layer_sizes = (400, 300) agent = DDQNAgent(env, state_transformer, 0, hidden_layer_sizes=hidden_layer_sizes, batch_size=32, train_start=20000, epsilon=0.7, fill_mem=True, render=False, pretrain_path=pretrain, train_path=train) with tf.Session() as sess: agent.set_session(sess) sess.run(tf.global_variables_initializer()) agent.serial_pretrain() #agent.load_pretrain_weights() agent.train(episodes=10000)
def main():
np.random.seed(12345678)
n = 10
env = PermutationSorting(n)
state_transformer = OneHotStateTransformer(n)
agent = DDQNAgent(env, state_transformer)
agent.parallel_pretrain(rows=10000, epochs=30)
# agent.load_pretrain_weights()
agent.train(episodes=10000, max_steps=250)
# agent.load_final_weights()
for _ in range(10):
p = np.random.permutation(n)
rl_ans = agent.solve(p)
exact_ans = PermutationExactSolver(n).solve(p)
print(p, '-', 'RL:', rl_ans, ' Exact:', exact_ans)
def ddpg_15():
n = 15
neighbors = 0.8
length = 1000
env = PermutationSorting(n, transpositions=True)
state_transformer = OneHotStateTransformer(n)
rt = np.arange(1, 16)
actor_layer_sizes = [400, 300]
critic_layer_sizes = [400, 300]
ddpg = DDPGAgent(env,
state_transformer,
actor_layer_sizes,
critic_layer_sizes,
batch_size=32,
actor_learning_rate=0.0001,
critic_learning_rate=0.001,
train_start=60000,
maxlen=1e6,
neighbors_percent=neighbors,
render=False,
train_path='./final_weights/ddpg_tf_final_weights_' +
str(n) + '_' + str(neighbors) + '.h5')
with tf.Session() as sess:
ddpg.set_session(sess)
ddpg.load_weights()
for revt in rt:
path_db = "./data/db15/perms-10k-r" + str(revt) + "-t" + str(revt)
path_res = "./data/15_wp/wp-1k-r" + str(revt) + "-t" + str(
revt) + ".dist"
start = time.time()
with open(path_db, 'r') as fp, open(path_res, 'w') as res:
line = fp.readline()
i = 0
while line and i < length:
p = np.fromstring(line, dtype=int, sep=',')
p = p - 1
ddpg_ans = ddpg.solve(p)
print(time.time() - start, i, ddpg_ans)
res.write(str(ddpg_ans) + '\n')
i += 1
line = fp.readline()
def ddpg_10():
n = 10
neighbors = 0.8
env = PermutationSorting(n, transpositions=True)
state_transformer = OneHotStateTransformer(n)
actor_layer_sizes = [400, 300]
critic_layer_sizes = [400, 300]
ddpg = DDPGAgent(env,
state_transformer,
actor_layer_sizes,
critic_layer_sizes,
batch_size=32,
actor_learning_rate=0.0001,
critic_learning_rate=0.001,
train_start=60000,
maxlen=1e6,
neighbors_percent=neighbors,
render=False,
train_path='./final_weights/ddpg_tf_final_weights_' +
str(n) + '_' + str(neighbors) + '.h5')
with tf.Session() as sess:
ddpg.set_session(sess)
ddpg.load_weights()
path_db = "./data/10_res/10_db"
path_res = "./data/10_res/wp.dist"
start = time.time()
i = 0
with open(path_db, 'r') as fp, open(path_res, 'w') as res:
line = fp.readline()
while line:
p = np.fromstring(line, dtype=int, sep=' ')
ddpg_ans = ddpg.solve(p)
print(time.time() - start, i, ddpg_ans)
res.write(str(ddpg_ans) + '\n')
i += 1
line = fp.readline()