os.environ['OMP_NUM_THREADS'] = '1'
args = parser.parse_args()
env = gym.make("FetchPickAndPlace-v1")
shared_model = Actor()
if args.use_cuda:
shared_model.cuda()
torch.cuda.manual_seed_all(12)
shared_model.share_memory()
if os.path.isfile(args.save_path1):
print('Loading A3C parametets ...')
shared_model.load_state_dict(torch.load(args.save_path1))
optimizer = SharedAdam(shared_model.parameters(), lr=args.lr)
optimizer.share_memory()
print("No of available cores : {}".format(mp.cpu_count()))
processes = []
counter = mp.Value('i', 0)
lock = mp.Lock()
print(counter)
p = mp.Process(target=test,
args=(args.num_processes, args, shared_model, counter))
p.start()
processes.append(p)
type=float,
default=0.9,
help='discount factor for rewards (default: 0.9)')
parser.add_argument('--tau',
type=float,
default=1.00,
help='parameter for GAE (default: 1.00)')
args = parser.parse_args()
model = Actor()
if args.use_cuda:
model.cuda()
torch.cuda.manual_seed_all(25)
optimizer = optim.Adam(model.parameters(), lr=0.0001)
if os.path.isfile(args.save_path1):
print('Loading A3C parametets ...')
model.load_state_dict(torch.load(args.save_path1))
for p in model.fc1.parameters():
p.requires_grad = False
for p in model.fc2.parameters():
p.requires_grad = False
FloatTensor = torch.cuda.FloatTensor if args.use_cuda else torch.FloatTensor
model.eval()
max_eps = 200000