self.epsilon *= self.epsilon_decay
def load(self, name):
self.model.load_weights(name)
def save(self, name):
self.model.save_weights(name)
if __name__ == "__main__":
env = TMSimEnv()
partition_list = {}
# for i in range(3):
# partition_list[i]=Partition(i, 0.2*(i+1))
g = Generation()
partition_list =g.generate_partitions(20)
print 'Number of partitions:'+str(len(partition_list))
env.make(partition_list, 0.6)
state_size = env.get_state_size()
action_size = env.get_action_size()
agent = DQNAgent(state_size, action_size)
# agent.load("./save/cartpole-dqn.h5")
done = False
batch_size = 32
wFile = open('Result_Diff_Task.txt','w')
for e in range(EPISODES):
state = env.reset()
r = 0
policies = ["best_fit", "first_fit", "worst_fit", "almost_worst_fit"] # ,
#policies = ["worst_fit"]
schedulability = []
total_jobs = []
failed_jobs = []
ratio = []
g = Generation()
#initialize counters
for i in range(len(policies)):
schedulability.append(0)
total_jobs.append(0)
failed_jobs.append(0)
ratio.append(0)
for i in range(repeat_times):
partition_list = g.generate_partitions(sum_af)
largest_aaf = max(partition_list, key=lambda x: x.af).af
sum_aaf = 0
for p in partition_list:
sum_aaf += p.aaf
print(sum_aaf)
task_list = g.generate_tasks(load_ratio * sum_aaf, False, 0.5, 0,
largest_aaf)
for j in range(len(policies)):
temp_partition_list = copy.deepcopy(partition_list)
temp_task_list = copy.deepcopy(task_list)
temp_fail, temp_total = run_test(temp_partition_list,
temp_task_list, pcpu_num,
simulation_time, policies[j])
while temp_fail is None or temp_total is None:
#print("not schedulable")
