def train_ddpg():
args = DDPGArgs()
env = gym.make(args.env_name)
agent = DDPGAgent(env, DDPGQNet, DDPGActor, SimpleNormalizer, args)
for ep in range(args.max_ep):
agent.train_one_episode()
if ep % args.test_interval == 0:
agent.test_model()
def train_ddpg_with_onecar_oneuav():
args = DDPGArgs()
env = Env()
agent = DDPGAgent(env, DDPGQNet, DDPGActor, SimpleNormalizer, args)
max_reward = 0
for ep in range(args.max_ep):
agent.train_one_episode()
if ep % args.test_interval == 0:
mean_reward = agent.test_model()
if mean_reward > max_reward:
max_reward = mean_reward
print('max_reward:{}'.format(max_reward))
dir = './result/ddpg/' + args.env_name + '/'
if not os.path.exists(dir):
os.makedirs(dir)
agent.save(dir)
env.close()
