high = np.inf * np.ones(4)
observation_space = Box(low=-high, high=high, dtype=np.float32)
high = np.ones(1)
action_space = Box(low=-high, high=high)
pol_net = PolNetLSTM(observation_space, action_space, h_size=args.h_size, cell_size=args.cell_size)
pol = GaussianPol(observation_space, action_space, pol_net, data_parallel=args.data_parallel, rnn=True)
if args.pol:
pol.load_state_dict(torch.load(args.pol, map_location=lambda storage, loc: storage))
else:
raise Exception
pol.to(device)
pol.dp_run = False
pol.reset()
r.set('start', 'false')
while True:
if r.get('start').decode('utf-8') == 'true':
break
class Process(object):
def run(self):
joint_pendulum = float(r.get('joint_info'))
joint_pendulum_vel = 0
self.action_input = 0
first_ob = [np.cos(joint_pendulum), np.sin(joint_pendulum), joint_pendulum_vel,
while args.max_epis > total_epi:
with measure('sample'):
epis = sampler.sample(pol, max_steps=args.max_steps_per_iter)
with measure('train'):
traj = Traj()
traj.add_epis(epis)
traj = ef.compute_vs(traj, vf)
traj = ef.compute_rets(traj, args.gamma)
traj = ef.compute_advs(traj, args.gamma, args.lam)
traj = ef.centerize_advs(traj)
traj = ef.compute_h_masks(traj)
traj.register_epis()
if args.data_parallel:
pol.dp_run = True
vf.dp_run = True
if args.ppo_type == 'clip':
result_dict = ppo_clip.train(traj=traj,
pol=pol,
vf=vf,
clip_param=args.clip_param,
optim_pol=optim_pol,
optim_vf=optim_vf,
epoch=args.epoch_per_iter,
batch_size=args.batch_size if
not args.rnn else args.rnn_batch_size,
max_grad_norm=args.max_grad_norm)
else:
result_dict = ppo_kl.train(traj=traj,
while args.max_epis > total_epi:
with measure('sample'):
epis = sampler.sample(pol, max_steps=args.max_steps_per_iter)
with measure('train'):
traj = Traj()
traj.add_epis(epis)
traj = ef.compute_vs(traj, vf)
traj = ef.compute_rets(traj, args.gamma)
traj = ef.compute_advs(traj, args.gamma, args.lam)
traj = ef.centerize_advs(traj)
traj = ef.compute_h_masks(traj)
traj.register_epis()
if args.data_parallel:
pol.dp_run = True
vf.dp_run = True
result_dict = ppo_clip.train(traj=traj, pol=pol, vf=vf, clip_param=args.clip_param,
optim_pol=optim_pol, optim_vf=optim_vf, epoch=args.epoch_per_iter, batch_size=args.batch_size if not args.rnn else args.rnn_batch_size, max_grad_norm=args.max_grad_norm)
total_epi += traj.num_epi
step = traj.num_step
total_step += step
rewards = [np.sum(epi['rews']) for epi in epis]
mean_rew = np.mean(rewards)
logger.record_results(args.log, result_dict, score_file,
total_epi, step, total_step,
rewards,
plot_title=args.env_name)
