text stringlengths 1 93.6k |
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d = DocHTTPRequestResponse(
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protocol="http",
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host="foobar.com",
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port=80
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)
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d.add_request_header("User-Agent: foobar")
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d.add_request_parameter("body", "action", "add")
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d.add_request_parameter("body", "doc", "456")
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d.add_request_parameter("body", "content", "Test")
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d.add_request_parameter("body", "csrftoken", "trulyrandom")
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d.add_response_header("X-Frame-Options: SAMEORIGIN")
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d.add_response_cookie("SESSIONID", "foobar1234")
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d.add_response_cookie("foo", "bar", "foobar.com", "/foo", datetime.now())
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d.request.method = "POST"
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d.response.body = "Added!"
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d.save()
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d = DocHTTPRequestResponse(
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protocol="http",
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host="foobar.com",
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port=80
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)
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d.add_request_header("User-Agent: foobar")
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d.add_request_parameter("body", "action", "delete")
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d.add_request_parameter("body", "doc", "456")
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d.add_response_header("X-Frame-Options: SAMEORIGIN")
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d.add_response_cookie("SESSIONID", "foobar1234")
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d.add_response_cookie("foo", "bar", "foobar.com", "/foo", datetime.now())
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d.request.method = "POST"
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d.response.body = "Deleted!"
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d.save()
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# <FILESEP>
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import gym
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.optim as optim
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from torch.distributions import Normal
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import numpy as np
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import collections, random
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#Hyperparameters
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lr_pi = 0.0005
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lr_q = 0.001
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init_alpha = 0.01
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gamma = 0.98
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batch_size = 32
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buffer_limit = 50000
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tau = 0.01 # for target network soft update
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target_entropy = -1.0 # for automated alpha update
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lr_alpha = 0.001 # for automated alpha update
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class ReplayBuffer():
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def __init__(self):
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self.buffer = collections.deque(maxlen=buffer_limit)
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def put(self, transition):
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self.buffer.append(transition)
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def sample(self, n):
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mini_batch = random.sample(self.buffer, n)
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s_lst, a_lst, r_lst, s_prime_lst, done_mask_lst = [], [], [], [], []
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for transition in mini_batch:
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s, a, r, s_prime, done = transition
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s_lst.append(s)
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a_lst.append([a])
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r_lst.append([r])
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s_prime_lst.append(s_prime)
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done_mask = 0.0 if done else 1.0
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done_mask_lst.append([done_mask])
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return torch.tensor(s_lst, dtype=torch.float), torch.tensor(a_lst, dtype=torch.float), \
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torch.tensor(r_lst, dtype=torch.float), torch.tensor(s_prime_lst, dtype=torch.float), \
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torch.tensor(done_mask_lst, dtype=torch.float)
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def size(self):
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return len(self.buffer)
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class PolicyNet(nn.Module):
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def __init__(self, learning_rate):
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super(PolicyNet, self).__init__()
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self.fc1 = nn.Linear(3, 128)
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self.fc_mu = nn.Linear(128,1)
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self.fc_std = nn.Linear(128,1)
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self.optimizer = optim.Adam(self.parameters(), lr=learning_rate)
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self.log_alpha = torch.tensor(np.log(init_alpha))
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self.log_alpha.requires_grad = True
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self.log_alpha_optimizer = optim.Adam([self.log_alpha], lr=lr_alpha)
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def forward(self, x):
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x = F.relu(self.fc1(x))
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mu = self.fc_mu(x)
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std = F.softplus(self.fc_std(x))
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dist = Normal(mu, std)
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action = dist.rsample()
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log_prob = dist.log_prob(action)
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