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
max_steps = 50
ep_numb = 0
done = True
success = 0
while ep_numb < max_eps:
ep_numb += 1
lastObs = env.reset()
goal = lastObs['desired_goal']
objectPos = lastObs['observation'][3:6]
object_rel_pos = lastObs['observation'][6:9]
object_oriented_goal = object_rel_pos.copy()
object_oriented_goal[
def test(rank, args, shared_model, counter):
FloatTensor = torch.cuda.FloatTensor if args.use_cuda else torch.FloatTensor
env = gym.make("FetchPickAndPlace-v1")
env2 = gym.wrappers.FlattenDictWrapper(
env, dict_keys=['observation', 'desired_goal'])
model = Actor()
if args.use_cuda:
model.cuda()
model.eval()
done = True
savefile = os.getcwd() + '/train/mario_curves.csv'
title = ['No. episodes', 'No. of success']
with open(savefile, 'a', newline='') as sfile:
writer = csv.writer(sfile)
writer.writerow(title)
while True:
model.load_state_dict(shared_model.state_dict())
ep_num = 0
num_ep = counter.value
success = 0
while ep_num < 100:
ep_num += 1
lastObs = env.reset()
goal = lastObs['desired_goal']
objectPos = lastObs['observation'][3:6]
object_rel_pos = lastObs['observation'][6:9]
object_oriented_goal = object_rel_pos.copy()
object_oriented_goal[
2] += 0.03 # first make the gripper go slightly above the object
timeStep = 0
model.load_state_dict(shared_model.state_dict())
#Ratio, first_step =[], []
while np.linalg.norm(
object_oriented_goal
) >= 0.015 and timeStep <= env._max_episode_steps:
env.render()
action = [0, 0, 0, 0, 0, 0]
object_oriented_goal = object_rel_pos.copy()
object_oriented_goal[2] += 0.03
for i in range(len(object_oriented_goal)):
action[i] = object_oriented_goal[i] * 6
action[3] = 0.05 #open
obsDataNew, reward, done, info = env.step(action)
timeStep += 1
objectPos = obsDataNew['observation'][3:6]
object_rel_pos = obsDataNew['observation'][6:9]
while np.linalg.norm(
object_rel_pos
) >= 0.005 and timeStep <= env._max_episode_steps:
env.render()
action = [0, 0, 0, 0, 0, 0]
for i in range(len(object_rel_pos)):
action[i] = object_rel_pos[i] * 6
action[4] = obsDataNew['observation'][13] / 8
action[3] = -0.01
obsDataNew, reward, done, info = env.step(action)
timeStep += 1
objectPos = obsDataNew['observation'][3:6]
object_rel_pos = obsDataNew['observation'][6:9]
state_inp = torch.from_numpy(
env2.observation(obsDataNew)).type(FloatTensor)
while np.linalg.norm(
goal -
objectPos) >= 0.01 and timeStep <= env._max_episode_steps:
env.render()
action = [0, 0, 0, 0, 0, 0]
act_tensor, ratio = act(state_inp, model, False, True)
#Ratio.append(ratio.cpu().detach().numpy())
for i in range(3):
action[i] = act_tensor[i].cpu().detach().numpy()
action[3] = -0.01
obsDataNew, reward, done, info = env.step(action)
timeStep += 1
state_inp = torch.from_numpy(
env2.observation(obsDataNew)).type(FloatTensor)
objectPos = obsDataNew['observation'][3:6]
object_rel_pos = obsDataNew['observation'][6:9]
if timeStep >= env._max_episode_steps: break
while True: #limit the number of timesteps in the episode to a fixed duration
env.render()
action = [0, 0, 0, 0, 0, 0]
action[3] = -0.01 # keep the gripper closed
obsDataNew, reward, done, info = env.step(action)
timeStep += 1
objectPos = obsDataNew['observation'][3:6]
object_rel_pos = obsDataNew['observation'][6:9]
if timeStep >= env._max_episode_steps: break
if info['is_success'] == 1.0:
success += 1
if done:
#plot_ratio = np.average(np.array(Ratio), 0)
#lastObs = env.reset()
if ep_num % 100 == 0:
print("num episodes {}, success {}".format(
num_ep, success))
data = [counter.value, success]
with open(savefile, 'a', newline='') as sfile:
writer = csv.writer(sfile)
writer.writerows([data])
time.sleep(20)