def main():
pa = parameters.Parameters() # 初始化参数
type_exp = 'pg_re' # 'pg_su' 'pg_su_compact' 'v_su', 'pg_v_re', 'pg_re', q_re', 'test'
pg_resume = None
v_resume = None
q_resume = None
log = None
render = False
try:
opts, args = getopt.getopt(
sys.argv[1:],
"hi:o:", ["exp_type=",
"num_res=",
"num_nw=",
"simu_len=",
"num_ex=",
"num_seq_per_batch=",
"eps_max_len=",
"num_epochs=",
"time_horizon=",
"res_slot=",
"max_job_len=",
"max_job_size=",
"new_job_rate=",
"dist=",
"lr_rate=",
"ba_size=",
"pg_re=",
"v_re=",
"q_re=",
"out_freq=",
"ofile=",
"log=",
"render=",
"unseen="])
except getopt.GetoptError:
script_usage()
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
script_usage()
sys.exit()
elif opt in ("-e", "--exp_type"):
type_exp = arg
elif opt in ("-n", "--num_res"):
pa.num_res = int(arg)
elif opt in ("-w", "--num_nw"):
pa.num_nw = int(arg)
elif opt in ("-s", "--simu_len"):
pa.simu_len = int(arg)
elif opt in ("-n", "--num_ex"):
pa.num_ex = int(arg)
elif opt in ("-sp", "--num_seq_per_batch"):
pa.num_seq_per_batch = int(arg)
elif opt in ("-el", "--eps_max_len"):
pa.episode_max_length = int(arg)
elif opt in ("-ne", "--num_epochs"):
pa.num_epochs = int(arg)
elif opt in ("-t", "--time_horizon"):
pa.time_horizon = int(arg)
elif opt in ("-rs", "--res_slot"):
pa.res_slot = int(arg)
elif opt in ("-ml", "--max_job_len"):
pa.max_job_len = int(arg)
elif opt in ("-ms", "--max_job_size"):
pa.max_job_size = int(arg)
elif opt in ("-nr", "--new_job_rate"):
pa.new_job_rate = float(arg)
elif opt in ("-d", "--dist"):
pa.discount = float(arg)
elif opt in ("-l", "--lr_rate"):
pa.lr_rate = float(arg)
elif opt in ("-b", "--ba_size"):
pa.batch_size = int(arg)
elif opt in ("-p", "--pg_re"):
pg_resume = arg
elif opt in ("-v", "--v_re"):
v_resume = arg
elif opt in ("-q", "--q_re"):
q_resume = arg
elif opt in ("-f", "--out_freq"):
pa.output_freq = int(arg)
elif opt in ("-o", "--ofile"):
pa.output_filename = arg
elif opt in ("-lg", "--log"):
log = arg
elif opt in ("-r", "--render"):
render = (arg == 'True')
elif opt in ("-u", "--unseen"):
pa.generate_unseen = (arg == 'True')
else:
script_usage()
sys.exit()
pa.compute_dependent_parameters()
if type_exp == 'pg_su':
pg_su.launch(pa, pg_resume, render, repre='image', end='all_done')
elif type_exp == 'v_su':
v_su.launch(pa, v_resume, render)
elif type_exp == 'pg_re':
pg_re.launch(pa, pg_resume, render, repre='image', end='all_done')
elif type_exp == 'pg_v_re':
pg_v_re.launch(pa, pg_resume, v_resume, render)
elif type_exp == 'test':
# quick_test.launch(pa, pg_resume, render)
slow_down_cdf.launch(pa, pg_resume, render, True)
# elif type_exp == 'q_re':
# q_re.launch(pa, q_resume, render)
else:
print("Error: unkown experiment type " + str(type_exp))
exit(1)
def main():
pa = parameters.Parameters()
type_exp = 'pg_re' # 'pg_su' 'pg_su_compact' 'v_su', 'pg_v_re', 'pg_re', q_re', 'test'
pg_resume = None
v_resume = None
q_resume = None
log = None
render = True
# try:
# opts, args = getopt.getopt(
# sys.argv[1:],
# "hi:o:", ["exp_type=",
# "num_res=",
# "num_nw=",
# "simu_len=",
# "num_ex=",
# "num_seq_per_batch=",
# "eps_max_len=",
# "num_epochs=",
# "time_horizon=",
# "res_slot=",
# "max_job_len=",
# "max_job_size=",
# "new_job_rate=",
# "dist=",
# "lr_rate=",
# "ba_size=",
# "pg_re=",
# "v_re=",
# "q_re=",
# "out_freq=",
# "ofile=",
# "log=",
# "render=",
# "unseen="])
#
# except getopt.GetoptError:
# script_usage()
# sys.exit(2)
#
# for opt, arg in opts:
# if opt == '-h':
# script_usage()
# sys.exit()
# elif opt in ("-e", "--exp_type"):
# type_exp = arg
# elif opt in ("-n", "--num_res"):
# pa.num_res = int(arg)
# elif opt in ("-w", "--num_nw"):
# pa.num_nw = int(arg)
# elif opt in ("-s", "--simu_len"):
# pa.simu_len = int(arg)
# elif opt in ("-n", "--num_ex"):
# pa.num_ex = int(arg)
# elif opt in ("-sp", "--num_seq_per_batch"):
# pa.num_seq_per_batch = int(arg)
# elif opt in ("-el", "--eps_max_len"):
# pa.episode_max_length = int(arg)
# elif opt in ("-ne", "--num_epochs"):
# pa.num_epochs = int(arg)
# elif opt in ("-t", "--time_horizon"):
# pa.time_horizon = int(arg)
# elif opt in ("-rs", "--res_slot"):
# pa.res_slot = int(arg)
# elif opt in ("-ml", "--max_job_len"):
# pa.max_job_len = int(arg)
# elif opt in ("-ms", "--max_job_size"):
# pa.max_job_size = int(arg)
# elif opt in ("-nr", "--new_job_rate"):
# pa.new_job_rate = float(arg)
# elif opt in ("-d", "--dist"):
# pa.discount = float(arg)
# elif opt in ("-l", "--lr_rate"):
# pa.lr_rate = float(arg)
# elif opt in ("-b", "--ba_size"):
# pa.batch_size = int(arg)
# elif opt in ("-p", "--pg_re"):
# pg_resume = arg
# elif opt in ("-v", "--v_re"):
# v_resume = arg
# elif opt in ("-q", "--q_re"):
# q_resume = arg
# elif opt in ("-f", "--out_freq"):
# pa.output_freq = int(arg)
# elif opt in ("-o", "--ofile"):
# pa.output_filename = arg
# elif opt in ("-lg", "--log"):
# log = arg
# elif opt in ("-r", "--render"):
# render = (arg == 'True')
# elif opt in ("-u", "--unseen"):
# pa.generate_unseen = (arg == 'True')
# else:
# script_usage()
# sys.exit()
# # supervised learning
# pa.type_exp = "pg_su"
# pa.simu_len = 50 # length of the busy cycle that repeats itself
# pa.num_ex = 10 # number of sequences
# pa.output_filename = "data/pg_su"
# pa.output_freq = 10
# pa.compute_dependent_parameters()
# pg_su.launch(pa, pg_resume, render, repre='image', end='all_done')
# reinforcement learning
# pa.type_exp = "pg_re_single_core"
pa.pg_resume = None #"data/pg_su_net_file_9990.pkl"
pa.simu_len = 50
pa.num_ex = 3
pa.output_filename = "data/pg_re"
pg_re.launch(pa)