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dqn_randomproblem.py
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dqn_randomproblem.py
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# -*- coding: utf-8 -*-
import random
from collections import deque
import keras
import numpy as np
from keras.layers import Dense, Input
from keras.models import Model, Sequential
from keras.optimizers import Adam
from keras.utils import plot_model
import JobShop
EPISODES = 10000
class DQNAgent:
# class for deep q learning agent
def __init__(self, state_size, action_size, number_job, number_feature):
self.state_size = state_size
self.action_size = action_size
self.number_job = number_job
self.number_feature = number_feature
self.memory = deque(maxlen=2000)
self.gamma = 0.95 # discount rate
self.epsilon = 0.9 # exploration rate
self.epsilon_min = 0.01
self.epsilon_decay = 0.995
self.learning_rate = 0.0005
self.model = self._build_subproblem_model() # build the model
def _build_subproblem_model(self):
# to build the whole model for jobshop
basic_model = self._submodel()
output_list = []
input_list = []
for i in range(self.number_job):
input_list.append(Input(shape=(self.number_feature,)))
output_list.append(basic_model(input_list[i]))
concatenated = keras.layers.concatenate(output_list)
out = Dense(self.action_size, activation='linear')(concatenated)
model = Model(input_list, out)
model.compile(loss='mse',
optimizer=Adam(lr=self.learning_rate))
return model
def _submodel(self):
# the sub model called by function _build_subproblem_model
model = Sequential(name='basic_model')
model.add(Dense(24, input_dim=self.number_feature, activation='relu'))
model.add(Dense(24, input_dim=self.number_feature, activation='relu'))
model.add(Dense(24, input_dim=self.number_feature, activation='relu'))
model.add(Dense(24, activation='relu'))
model.add(Dense(1, activation='linear'))
model.compile(loss='mse',
optimizer=Adam(lr=self.learning_rate))
return model
def _easymodel(self):
# the easy ann model, not used in this method
model = Sequential()
model.add(Dense(24, input_dim=self.state_size, activation='relu'))
model.add(Dense(24, input_dim=self.state_size, activation='relu'))
model.add(Dense(self.action_size, activation='linear'))
model.compile(loss='mse',
optimizer=Adam(lr=self.learning_rate))
return model
def remember(self, state, action, reward, next_state, done):
# remember the information of this step
self.memory.append((state, action, reward, next_state, done))
def act(self, state):
# let the agent make a decision
# choose a job to process in current state
if np.random.rand() <= self.epsilon:
return random.randrange(self.action_size)
act_values = self.model.predict(state)
return np.argmax(act_values[0]) # returns action
def replay(self, batch_size):
# replay the history and train the model
minibatch = random.sample(self.memory, batch_size)
for state, action, reward, next_state, done in minibatch:
target = reward
if not done:
target = (reward + self.gamma *
np.amax(self.model.predict(next_state)[0]))
target_f = self.model.predict(state)
target_f[0][action] = target
self.model.fit(state, target_f, epochs=1, verbose=0)
if self.epsilon > self.epsilon_min:
self.epsilon *= self.epsilon_decay
def load(self, name):
# load the model
self.model.load_weights(name)
def save(self, name):
# save the model
self.model.save_weights(name)
if __name__ == "__main__":
# agent.load("./save/jobshop-dqn.h5")
number_job = 5
number_machine = 4
number_feature = 2
state_size = number_job * number_feature
action_size = number_job
agent = DQNAgent(state_size, action_size, number_job, number_feature)
batch_size = number_job * number_machine * 10
history = []
successnumber = 0
# the main loop for each job shop problem
for e in range(EPISODES):
problem = JobShop.JobShop(number_machine, number_job, 15, 30, True)
state, score, done = problem.Step()
action_list = []
oldscore = 0
score = 0
# the sub loop for each step of the problem
for time in range(number_job*number_machine):
action = agent.act(state)
next_state, score, done = problem.Step(action)
reward = oldscore - score + 15 if not done else -1000
oldscore = score
agent.remember(state, action, reward, next_state, done)
state = next_state
if done:
if time >= number_job * number_machine-1:
successnumber += 1
break
# record the history
action_list.append(action)
if len(agent.memory) > batch_size:
agent.replay(batch_size)
# problem.PlotResult()
if e % 10 == 0:
print("loop : {}/{}, makespan: {} success: {} / 10, e: {:.2}"
.format(e, EPISODES, score, successnumber, agent.epsilon))
print(action_list, len(action_list))
f = open('log/logs', 'a')
f.close()
successnumber = 0
agent.save("./save/jobshop-dqn.h5")