net.load_simple_network(restore_path + "actor.pth")
# net.load_replay_buffer(restore_path+"replay.pkl") # 因为文件太大了，我删掉了默认的值
net.load_norm(restore_path + "norm.pkl")
trainer(net, env, args)
if __name__ == '__main__':
# take the configuration for the HER
# os.environ['OMP_NUM_THREADS'] = '1'
# os.environ['MKL_NUM_THREADS'] = '1'
# os.environ['IN_MPI'] = '1'
# get the params
args = get_args()
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu_id)
mpi_fork(args.cpu)
from algos.tf1.td3_sp.TD3_per_her import TD3
from algos.tf1.ddpg_sp.DDPG_per_her import DDPG
from algos.tf1.sac_sp.SAC_per_her import SAC
from algos.tf1.sac_auto.sac_auto_per_her import SAC_AUTO
from algos.pytorch.td3_sp.td3_per_her import TD3Torch
from algos.pytorch.ddpg_sp.ddpg_per_her import DDPGTorch
from algos.pytorch.sac_sp.sac_per_her import SACTorch
RL_list = [TD3, DDPG, SAC, SAC_AUTO, TD3Torch, DDPGTorch, SACTorch]
[
launch(net=net, args=args) for net in RL_list
if net.__name__ == args.RL_name
]
def thunk_plus():
# Fork into multiple processes
mpi_fork(cpu_num)
# Run thunk
thunk(thunk_params_dict_list)