def train(config, network_spec=None):
data_provider = DataProvider(config.db)
env = StockEnvironment(data_provider, config, 0)
agent = overwrite_agent(env, network_spec,
config) if config.overwrite_agent else load_agent(
config, env, network_spec)
mlflow.log_param("agent", "tensorforce.agents.DQNAgent")
for key in config.agent_specs:
mlflow.log_param(key, config.agent_specs[key])
runner = Runner(agent=agent, environment=env)
offset = 20000
num_episodes = 20
step = 0
while data_provider.has_data_key(offset + config.max_step_per_episode):
runner.run(num_episodes=num_episodes)
offset = offset + config.max_step_per_episode
env.offset = offset
agent.save(config.agent_dir, config.agent_name)
if step % 10 == 0:
evaluate(config, data_provider,
offset - config.max_step_per_episode, agent)
step += 1
return agent, env
def post_detect(target, ck, flag, param, waf):
payload = load()
agent = load_agent()
host = target
for i in range(0, len(payload)):
if waf == True:
time.sleep(0.5)
try:
urllib2.urlopen(host)
except:
pass
time.sleep(0.5)
for j in param.keys():
if flag in param[j]:
exp = flag + payload[i]
param[j] = param[j].replace(flag, exp)
try:
post_data = load_post_data(agent, ck, param)
req = urllib2.Request(target, data=post_data)
res = urllib2.urlopen(req)
content_html = res.read()
if exp in content_html or payload[i] in content_html:
print '[!]success wtih payload', param[j]
#post_detect(target,ck,flag,param_dict,True)
except:
pass
param[j] = param[j].replace(exp, flag)
def __init__(self, speed=0.02, size=720):
self.draw_next_offset = size / 4
width = size / 2
height = size # + self.draw_next_offset
self.root = Tk()
self.root.configure(background=COLORS[0])
self.game = Canvas(self.root, width=width, height=height, bg=COLORS[0])
self.game.pack()
self.env = Environment()
self.env.reset()
self.history = pickle.load(open(sys.argv[1], "rb"))
self.processed = []
"""
for state in self.history:
self.processed.append(self.process_channels(state))
"""
self.agent = Agent(6) if len(sys.argv) == 1 else load_agent(
sys.argv[1])
cnt = 0
rewards = []
for m in self.agent.replay_memory.memory:
rewards.append(m[3])
idx = np.argmax(rewards)
print(self.agent.replay_memory.memory[idx][0][2])
print(self.agent.replay_memory.memory[idx][4][2])
print("duration", max(self.agent.durations))
print("score", max(self.agent.scores))
print(min(rewards))
print(max(rewards))
self.speed = speed
self.size = size
self.rectangle_size = size / self.env.row
self.pause = False
self.quit = False
self.image_counter = 0
self.commands = {
113: 1, # Left
114: 2, # Right
53: 3, # Z
52: 4, # X
65: 5, # Drop
37: 0 # Do nothing
}
self.init()
self.root.title('Tetris')
self.root.bind("<Key>", self.key_down)
#threading.Thread(target=self.debug_channels).start()
#threading.Thread(target=self.watch_history).start()
#threading.Thread(target=self.play).start()
threading.Thread(target=self.watch_play).start()
self.root.mainloop()
def get_detect(target,ck,flag,waf):
payload = load()
agent = load_agent()
host = target.split('?')[0]
for i in range(0,len(payload)):
if waf == True:
time.sleep(0.5)
try:
urllib2.urlopen(host)
except:
pass
time.sleep(0.5)
exp = flag + str(payload[i])
now_target = target.replace(flag,exp)
#print now_target
try:
data = load_data(agent,ck)
req = urllib2.Request(now_target,data = data)
res = urllib2.urlopen(req)
content_html = res.read()
if waf == True:
time.sleep(0.5)
try:
urllib2.urlopen(host)
except:
pass
time.sleep(0.5)
#print content_html
if exp in content_html or str(payload[i]) in content_html:
#print 'aaa'
webbrowser.open(now_target)
print '[*]find payload success!',now_target
#exit()
else:
pass
#print '[*]find payload failed!',now_target
except:
pass
import os
import urllib
import urllib2
import urlparse
import requests
from get import get_detect
from post import post_detect
from common import load_data, load_post_data
from agent import load_agent
import re
from bs4 import BeautifulSoup
method = ''
flag = 'q0w1e2'
agent_list = load_agent()
def get_fuzzing(target, ck, data):
print '[*]now demo test get xss......'
parsed_tuple = urlparse.urlparse(urllib.unquote(target))
url_query = urlparse.parse_qs(parsed_tuple.query, True)
print url_query
for i in url_query.keys():
query = str(i) + '=' + str(url_query[i][0])
tmp = query + flag
location = str(url_query[i][0]) + flag
now_target = target.replace(query, tmp)
#data = load_data(agent_list,ck)
try:
load = args.load
env_name = 'Firefly-v1' # 'Pendulum-v0'
env = gym.make(env_name)
state_dim = env.observation_space.shape[0]
action_dim = env.action_space.shape[0]
memory_size = 1000000
num_episodes = 2000
num_steps = env.episode_len
batch_size = 64
std = 1.
agent = Agent(state_dim, action_dim, hidden_dim=64, tau=0.001)
noise = Noise(action_dim, mean=0., std=std)
#replay = ReplayMemory(memory_size)
if play:
agent = load_agent(file='pretrained/model_7.pth.tar')
if load:
agent = load_agent(file='pretrained/model_6.0.pth.tar') #6.0
rewards = []
for episode in range(num_episodes):
state = torch.Tensor([env.reset()])
episode_reward = 0.
std *= 0.9985
noise.reset(0., std)
for t in range(num_steps):
if not play:
action = agent.select_action(state, noise)
else:
action = agent.select_action(state)
#print(action)
from environment import Environment
from agent import Agent, load_agent
import torch
import numpy as np
import math
import pickle
import sys
num_actions = 6
num_iter = 50000000
print_interval = 10
save_interval = 200
env = Environment()
agent = Agent(num_actions) if len(sys.argv) == 1 else load_agent(sys.argv[1])
#agent.optimizer = torch.optim.Adam(agent.local_Q.parameters(), 5e-4)
print(agent.optimizer)
for episode in range(agent.start, num_iter):
done = False
score = 0
ep_duration = 0
state, next_piece = env.reset()
while not done:
action = agent.select_action(state, next_piece)
next_state, reward, done, next_next_piece = env.step(action)
agent.store_experience(state, next_piece, action, reward, next_state, next_next_piece, 1-done)
state = next_state
next_piece = next_next_piece
score += reward
env.goal_radius = 0.4
state_dim = env.observation_space.shape[0]
action_dim = env.action_space.shape[0]
memory_size = 1000000
num_episodes = 2000
num_steps = env.episode_len
batch_size = 64
std = 0.1
#agent = Agent(state_dim, action_dim, hidden_dim=64, tau=0.001)
noise = Noise(action_dim, mean=0., std=std)
#replay = ReplayMemory(memory_size)
gamma = Variable(torch.Tensor([0.99]), requires_grad=True)
rewards = []
times = []
agent = load_agent(file='pretrained/model_3.0.pth.tar', gamma=gamma)
for episode in range(20):
state = torch.Tensor([env.reset()])
episode_reward = 0.
#std *= 0.9985
noise.reset(0., std)
for t in range(num_steps):
action = agent.select_action(state, noise)
next_state, reward, done, _ = env.step(action.cpu().numpy()[0])
episode_reward += reward
#action = torch.Tensor(action)
mask = torch.Tensor([not done])
next_state = torch.Tensor([next_state])
reward = torch.Tensor([reward])
agent.memory.push(state, action, mask, next_state, reward)
state = next_state
def eval_():
config = MlConfig(agent_name="abcd-agent")
data_provider = DataProvider(config.db)
env = StockEnvironment(data_provider, config.max_step_per_episode, 0)
agent = load_agent(config, env, None)
evaluate(config, data_provider, 0, agent)