def main():
# argument parser to specify hyperparameters
parser = argparse.ArgumentParser()
parser.add_argument("--train", help="training run", action="store_true")
parser.add_argument("--test", help="test run", action="store_true")
parser.add_argument("--network_type",
default="mlp",
help="""type of the policy network ["mlp", "gru"]""",
type=str)
parser.add_argument(
"--num_agents",
default=8,
help="number of environments and agents running in parallel",
type=int)
parser.add_argument(
"--num_steps",
default=32,
help="number of steps on each environment for every update",
type=int)
parser.add_argument("--environment",
default="LunarLanderContinuous-v2",
help="gym environment type",
type=str)
args = parser.parse_args()
# Launch tensorboard
tb = program.TensorBoard()
tb.configure(argv=[None, '--logdir', './logs'])
tb.launch()
# Initialize ray
ray.init(memory=1024 * 512 * 200, object_store_memory=1024 * 1024 * 1000)
if args.train:
# start training run with given hyperparameters
coord = Coordinator(num_agents=args.num_agents,
network=args.network_type,
env_name=args.environment,
num_steps=args.num_steps)
coord.train()
if args.test:
# start run with latest model checkpoint
from test import test_run
test_run(network=args.network_type, environment=args.environment)
ray.shutdown()
import os
import sys
current_path = os.getcwd()
sys.path.append(current_path)
from hypertune import start_commander, start_workers
from coordinator import Coordinator
from config import mode, number_workers, tuned_config, fix_random_seed
import numpy as np
if fix_random_seed:
np.random.seed(123)
# %matplotlib inline
if mode == 'parallel':
start_commander()
workers = start_workers(number_workers)
else:
model = Coordinator(tuned_config, '2900')
#################### to do ####################
# model = Coordinator(tuned_config, '-5.28')
# model.restore_price_predictor('-5.28-80000-')
##############################################
model.train('single', True)
model.back_test('test', 2500, True)
coo = Coordinator(agent)
env = PortfolioEnv(df_train,
steps=256,
trading_cost=0.00007,
window_length=window_length,
scale=False,
random_reset=False)
ob = env.reset()
for i in range(5):
print(coo.action_values(ob))
ob, a, r, ob_ = env.step(np.ones(5))
coo.train(env,
total_training_step=total_training_step,
replay_period=replay_period,
tensorboard=True)
env_test = PortfolioEnv(df_train,
steps=2500,
trading_cost=0.0,
window_length=window_length,
scale=False,
random_reset=False)
ob = env_test.reset()
for i in range(10):
print(coo.action_values(ob))
print(np.argmax(coo.action_values(ob)))
ob, a, r, ob_ = env.step(np.ones(5))
# coo.restore('')
import os
import sys
current_path = os.getcwd()
sys.path.append(current_path)
from coordinator import Coordinator
from config import tuned_config
model = Coordinator(tuned_config, 'name')
model.train()
model.restore('name-step')
