def train_dqn():
args = DQNArgs()
env = gym.make(args.env_name)
agent = DQNAgent(env, QNet, SimpleNormalizer, args)
pre_best = -1e9
for ep in range(args.max_ep):
agent.train_one_episode()
if ep % args.test_interval == 0:
r = agent.test_model()
if r > pre_best:
pre_best = r
agent.save(args.save_dir)
