parser.add_argument("--cfg_path", type=str, default='config.json.dist')
parser.add_argument("--restrict", type=bool, default=False)
parser.add_argument("--imitation", type=bool, default=False)
parser.add_argument("--test", type=bool, nargs='?', const=True, default=False)
parser.add_argument("--restore", type=bool, nargs='?', const=True, default=False)
parser.add_argument('--save_replay', type=bool, nargs='?', const=True, default=False)
args = parser.parse_args()
os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu) #environ是一个字符串所对应环境的映像对象，输入为要使用的GPU number
tf.reset_default_graph()
sess = tf.Session()
# config = Config(args.sz, args.map, lambda _: 1)
config = Config(args.sz, args.map, args.run_id, restrict=args.restrict, imitation=args.imitation) # 进行参数的设置
os.makedirs('weights/' + config.full_id(), exist_ok=True)
cfg_path = 'weights/%s/config.json' % config.full_id() # 保存参数的位置
config.build(cfg_path if args.restore else args.cfg_path) # 建立和设置参数
if not args.restore and not args.test:
config.save(cfg_path) # 保存参数
envs = EnvWrapper(make_envs(args), config) # 创建环境,封装一层
agent = A2CAgent(sess, fully_conv, config, args.restore, args.discount, args.lr, args.vf_coef, args.ent_coef, args.clip_grads) # 创建agent
runner = Runner(envs, agent, args.steps) # 创建进程
runner.run(args.updates, not args.test) # 开始运行
if args.save_replay: #是否保存回放
envs.save_replay()
envs.close() # 关闭环境
parser.add_argument("--test",
type=bool,
nargs='?',
const=True,
default=False)
parser.add_argument("--restore",
type=bool,
nargs='?',
const=True,
default=False)
args = parser.parse_args()
os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu)
tf.reset_default_graph()
sess = tf.Session()
envs = EnvWrapper(make_envs(args))
# not specify which feature to use when initialization
if args.game == 'CartPole-v0':
num_action = 2
input_size = None
agentmodel_local = carpole_net_local
agentmodel_target = carpole_net_target
if args.game == 'Breakout-v0':
num_action = 4
input_size = 80 * 80
agentmodel_local = Breakout_local
agentmodel_target = Breakout_target
if args.game == 'MsPacman-v0':
num_action = 9
input_size = [88, 80]
agentmodel_local = MsPacman_local
runner = Runner(None, agent, args, args.steps)
NextStateValue = runner.forwardState()
np.save(args.nextStatePath+'nextState_'+str(oneCkpt.split('-')[1]) + '.npy', NextStateValue)
except:
pass
time.sleep(10)
os._exit(0)
else:
args.ckptfile = None
# begin to train the agent
if not args.distill:
try:
sess = tf.Session(config=tf_config)
envs = EnvWrapper(make_envs(args), config)
agent = A2CAgent(sess, fully_conv, config, args.discount, args.lr, args.vf_coef, args.ent_coef, args.clip_grads,
weight_dir, log_dir, args)
runner = Runner(envs, agent, args, args.steps)
except Exception as e:
print('--exit--')
print(e)
if envs is not None:
envs.close()
import sys
sys.exit(-1)
runner.run(args.updates, not args.test)
if args.save_replay:
from agents.exploration import Explore
agent = Explore(args, device)
args.infer = False
args.train = False
elif args.meta == 'hrl':
from agents.hrl import HRL
from agents.model import FullyConv
actor_critic = FullyConv(config, device, args.envs)
agent = HRL(actor_critic, config, args)
args.infer = False
args.train = True
else:
raise ValueError
# initialize training setups
envs = EnvWrapper(make_envs(args), config, args, device)
sc2_filewriter = SC2FileWriter(args, dirname)
runner = Runner(envs, agent, ilp, sc2_filewriter, config, args, device, dirname)
time.sleep(5) # wait for SC2 to boot up
# run eval or train
if args.eval and not args.load_ilp: # hrl, random
runner.eval(num_iter=args.num_iter)
else:
if args.meta=='MSGI' or args.meta=='hrl': # MSGI, HRL
if args.load_ilp:
runner.meta_eval_load(dirname, init_ep=args.init_ep)
else:
runner.meta_eval_save(num_iter=args.num_iter, tr_epi=args.tr_epi)
# save game replay