def __init__(self, n_agents, map_name='default_small'):
self.n_agents = n_agents
self.env = GatheringEnv(n_agents)
type=int,
default=543,
metavar='N',
help='random seed (default: 543)')
parser.add_argument('--render',
default=True,
action='store_true',
help='render the environment')
parser.add_argument('--log-interval',
type=int,
default=2,
metavar='N',
help='interval between training status logs (default: 10)')
args = parser.parse_args()
env = GatheringEnv(2,
"default_small2") #"mini_map") # gym.make('CartPole-v1')
env.seed(args.seed)
torch.manual_seed(args.seed)
# agentParam =
model_name = "gathering_social_" #gathering_1"
file_name = "save_weight/" + model_name
ifload = False
save_eps = 20
ifsave_model = True
# logger = Logger('./logs5')
agentParam = {
"gamma": args.gamma,
"LR": 1e-2,
"device": device,
type=int,
default=543,
metavar='N',
help='random seed (default: 543)')
parser.add_argument('--render',
action='store_true',
help='render the environment')
parser.add_argument('--log-interval',
type=int,
default=5,
metavar='N',
help='interval between training status logs (default: 10)')
args = parser.parse_args()
n_agents = 2
env = GatheringEnv(n_agents, "default_small2")
env.seed(args.seed)
torch.manual_seed(args.seed)
test_mode = False
names = {
"social": "pg_social",
"base": "pg_indi",
"single": "pg_single_less",
"forbid": "base_forbid"
}
mode = "base"
model_name = names[mode]
file_name = "train_para/" + model_name
agentParam = {
type=int,
default=543,
metavar='N',
help='random seed (default: 543)')
parser.add_argument('--render',
default=True,
action='store_true',
help='render the environment')
parser.add_argument('--log-interval',
type=int,
default=2,
metavar='N',
help='interval between training status logs (default: 10)')
args = parser.parse_args()
env = GatheringEnv(2, "default_small2")
env.seed(args.seed)
torch.manual_seed(args.seed)
# agentParam =
model_name = "gathering_centIAC" #"gathering_social_share"#"gathering_social_v1"#gathering_1"
file_name = "save_weight/" + model_name
ifload = False
save_eps = 20
ifsave_model = True
# logger = Logger('./logs5')
agentParam = {
"gamma": args.gamma,
"LR": 1e-2,
"device": device,
def init_env():
env = env_wrapper(GatheringEnv(
2, "default_small2")) #env_wrapper(CleanupEnv(num_agents=4))
# env.seed(seed + rank * 1000)
np.random.seed(seed + rank * 1000)
return env
parser.add_argument('--render',
default=False,
action='store_true',
help='render the environment')
parser.add_argument(
'--log-interval',
type=int,
default=2,
metavar='N',
help='interval between training status logs0 (default: 10)')
args = parser.parse_args()
n_agents = 2
state_dim = 400
action_dim = 8
env = GatheringEnv(2, "default_small2")
torch.manual_seed(args.seed)
# agentParam =
model_name = "gathering_maac" #"gathering_centIAC" #"gathering_social_v1"#gathering_1"
file_name = "save_weight/" + model_name
ifload = False
save_eps = 30
ifsave_model = True
agentParam = {
"gamma": args.gamma,
"LR": 1e-2,
"device": device,
"ifload": ifload,
"filename": file_name
type=int,
default=543,
metavar='N',
help='random seed (default: 543)')
parser.add_argument('--render',
default=True,
action='store_true',
help='render the environment')
parser.add_argument('--log-interval',
type=int,
default=2,
metavar='N',
help='interval between training status logs (default: 10)')
args = parser.parse_args()
env = GatheringEnv(2) # gym.make('CartPole-v1')
env.seed(args.seed)
torch.manual_seed(args.seed)
agentParam = {"gamma": args.gamma, "LR": 1e-2, "device": device}
# agentParam =
model_name = "pg_social"
file_name = "/Users/xue/Desktop/Social Law/saved_weight/" + model_name
save_eps = 10
ifsave_model = True
# logger = Logger('./logs5')
class Agents():
def __init__(self, agents):