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brain_v3.py
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from collections import namedtuple
import random
import torch
from torch import nn
from torch import optim
import torch.nn.functional as F
from params import *
from copy import copy
import warnings
warnings.filterwarnings("ignore", category=UserWarning)
BATCH_SIZE = 8
CAPACITY = 10000
Transition = namedtuple('Transition', ('state1', 'state2', 'state3', 'action', 'next_state1', 'next_state2', 'next_state3', 'reward'))
class Agent_v3:
def __init__(self):
self.brain = Brain_v3()
def update_q_function(self):
return self.brain.replay()
def get_action(self, state, episode):
action = self.brain.decide_action(state, episode)
return action
def memorize(self, state, action, state_next, reward):
self.brain.memory.push(state, action, state_next, reward)
def update_target_q_function(self):
self.brain.update_target_q_network()
class ReplayMemory:
def __init__(self, CAPACITY):
self.capacity = CAPACITY
self.memory = []
self.index = 0
def push(self, state, action, state_next, reward):
if len(self.memory) < self.capacity:
self.memory.append(None)
self.memory[self.index] = Transition(state[0], state[1], state[2], action, state_next[0], state_next[1], state_next[2], reward)
self.index = (self.index + 1) % self.capacity
def sample(self, batch_size):
return random.sample(self.memory, batch_size)
def __len__(self):
return len(self.memory)
class Net_v3(nn.Module):
def __init__(self, n_in, n_mid, n_out):
super(Net_v3, self).__init__()
self.conv1 = nn.Conv2d(1, 1, 1) # (N_SERVER * N_JOB)
self.conv2 = nn.Conv2d(1, 1, 1) # (N_SERVER * N_JOB)
self.linear = nn.Linear(N_JOB, N_JOB) # (1 * N_JOB)
self.fc1 = nn.Linear((2*N_SERVER+1)*N_JOB, n_mid)
self.fc2 = nn.Linear(n_mid, n_mid)
self.fc3 = nn.Linear(n_mid, n_out)
def forward(self, x, y, z): # 输入state的三个信息
x = F.leaky_relu(self.conv1(y)) # (N_SERVER * N_JOB)
x = F.max_pool2d(x, 1) # (N_SERVER * N_JOB)
y = F.leaky_relu(self.conv2(y)) # (N_SERVER * N_JOB)
y = F.max_pool2d(y, 1) # (N_SERVER * N_JOB)
z = F.leaky_relu(self.linear(z)) # (1 * N_JOB)
# print(x.shape, y.shape, z.shape)
# print(torch.cat((x[0],y[0]), 1).shape)
k = torch.cat((x, y, z), 2)
batch = k.shape[0]
k = k.view(batch, (2*N_SERVER+1)*N_JOB)
h1 = F.leaky_relu(self.fc1(k))
h2 = F.leaky_relu(self.fc2(h1))
output = self.fc3(h2)
return output
class Brain_v3:
def __init__(self):
self.num_actions = N_SERVER
self.memory = ReplayMemory(CAPACITY)
n_in, n_mid, n_out = 32, 32, N_SERVER
if LOAD_OK1:
self.main_q_network = torch.load(PATH1)
self.target_q_network = torch.load(PATH1)
else:
self.main_q_network = Net_v3(n_in, n_mid, n_out).to(device)
self.target_q_network = Net_v3(n_in, n_mid, n_out).to(device)
self.optimizer = optim.Adam(
self.main_q_network.parameters(), lr=0.0001)
# print(self.main_q_network)
def replay(self):
if len(self.memory) < BATCH_SIZE:
return torch.Tensor([0.0]).to(device)
self.batch, self.state_batch, self.action_batch, self.reward_batch, self.non_final_next_states = self.make_minibatch()
self.expected_state_action_values = self.get_expected_state_action_values()
return self.update_main_q_network()
def decide_action(self, state, episode):
epsilon = 0.5 * (1 / (episode + 1))
if epsilon <= np.random.uniform(0, 1):
# if np.random.randint(1,10) >=2:
self.main_q_network.eval()
with torch.no_grad():
# print(state[0].shape, state[1].shape,state[2].shape)
action = self.main_q_network(state[0], state[1], state[2]).max(1)[1].view(1, 1)
else:
action = torch.LongTensor(
[[random.randrange(self.num_actions)]])
return action
def make_minibatch(self):
transitions = self.memory.sample(BATCH_SIZE)
batch = Transition(*zip(*transitions))
state_batch1 = torch.cat(batch.state1)
state_batch2 = torch.cat(batch.state2)
state_batch3 = torch.cat(batch.state3)
# print(batch.action)
action_batch = torch.cat(batch.action)
reward_batch = torch.cat(batch.reward)
non_final_next_states1 = torch.cat([s for s in batch.next_state1
if s is not None])
non_final_next_states2 = torch.cat([s for s in batch.next_state2
if s is not None])
non_final_next_states3 = torch.cat([s for s in batch.next_state3
if s is not None])
non_final_next_states = [non_final_next_states1, non_final_next_states2, non_final_next_states3]
state_batch = [state_batch1, state_batch2, state_batch3]
return batch, state_batch, action_batch, reward_batch, non_final_next_states
def get_expected_state_action_values(self):
self.main_q_network.eval()
self.target_q_network.eval()
# self.state_action_values = self.main_q_network(self.state_batch).cuda(device).gather(1, self.action_batch)
self.state_action_values = self.main_q_network(self.state_batch[0], self.state_batch[1], self.state_batch[2]).gather(1, self.action_batch.to(device)).to(device)
non_final_mask = torch.ByteTensor(tuple(map(lambda s: s is not None, self.batch.next_state1)))
next_state_values = torch.zeros(BATCH_SIZE).to(device)
a_m = torch.zeros(BATCH_SIZE).type(torch.LongTensor).to(device)
a_m[non_final_mask] = self.main_q_network(self.non_final_next_states[0], self.non_final_next_states[1], self.non_final_next_states[2]).detach().max(1)[1].to(device)
a_m_non_final_next_states = a_m[non_final_mask].view(-1, 1).to(device)
next_state_values[non_final_mask] = self.target_q_network(self.non_final_next_states[0], self.non_final_next_states[1], self.non_final_next_states[2]).gather(1, a_m_non_final_next_states).detach().squeeze()
# print(self.reward_batch)
# print(next_state_values)
expected_state_action_values = self.reward_batch + GAMMA * next_state_values.to(device)
return expected_state_action_values
def update_main_q_network(self):
self.main_q_network.train()
loss = F.smooth_l1_loss(self.state_action_values,
self.expected_state_action_values.unsqueeze(1))
# print("loss, ", loss)
ret = copy(loss)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
return ret
def update_target_q_network(self):
self.target_q_network.load_state_dict(self.main_q_network.state_dict())