def test_learning_rnn(self):
pol_net = PolNetLSTM(
self.env.observation_space, self.env.action_space, h_size=32, cell_size=32)
pol = GaussianPol(self.env.observation_space,
self.env.action_space, pol_net, rnn=True)
vf_net = VNetLSTM(self.env.observation_space, h_size=32, cell_size=32)
vf = DeterministicSVfunc(self.env.observation_space, vf_net, rnn=True)
sampler = EpiSampler(self.env, pol, num_parallel=1)
optim_pol = torch.optim.Adam(pol_net.parameters(), 3e-4)
optim_vf = torch.optim.Adam(vf_net.parameters(), 3e-4)
epis = sampler.sample(pol, max_steps=400)
traj = Traj()
traj.add_epis(epis)
traj = ef.compute_vs(traj, vf)
traj = ef.compute_rets(traj, 0.99)
traj = ef.compute_advs(traj, 0.99, 0.95)
traj = ef.centerize_advs(traj)
traj = ef.compute_h_masks(traj)
traj.register_epis()
result_dict = ppo_clip.train(traj=traj, pol=pol, vf=vf, clip_param=0.2,
optim_pol=optim_pol, optim_vf=optim_vf, epoch=1, batch_size=2)
result_dict = ppo_kl.train(traj=traj, pol=pol, vf=vf, kl_beta=0.1, kl_targ=0.2,
optim_pol=optim_pol, optim_vf=optim_vf, epoch=1, batch_size=2, max_grad_norm=20)
del sampler
def test_learning(self):
pol_net = PolNet(self.env.ob_space, self.env.ac_space, h1=32, h2=32)
pol = CategoricalPol(self.env.ob_space, self.env.ac_space, pol_net)
vf_net = VNet(self.env.ob_space, h1=32, h2=32)
vf = DeterministicSVfunc(self.env.ob_space, vf_net)
sampler = EpiSampler(self.env, pol, num_parallel=1)
optim_pol = torch.optim.Adam(pol_net.parameters(), 3e-4)
optim_vf = torch.optim.Adam(vf_net.parameters(), 3e-4)
epis = sampler.sample(pol, max_steps=32)
traj = Traj()
traj.add_epis(epis)
traj = ef.compute_vs(traj, vf)
traj = ef.compute_rets(traj, 0.99)
traj = ef.compute_advs(traj, 0.99, 0.95)
traj = ef.centerize_advs(traj)
traj = ef.compute_h_masks(traj)
traj.register_epis()
result_dict = ppo_clip.train(traj=traj, pol=pol, vf=vf, clip_param=0.2,
optim_pol=optim_pol, optim_vf=optim_vf, epoch=1, batch_size=32)
result_dict = ppo_kl.train(traj=traj, pol=pol, vf=vf, kl_beta=0.1, kl_targ=0.2,
optim_pol=optim_pol, optim_vf=optim_vf, epoch=1, batch_size=32, max_grad_norm=10)
del sampler
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,
pol=pol,
vf=vf,
kl_beta=kl_beta,
kl_targ=args.kl_targ,
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)
kl_beta = result_dict['new_kl_beta']
if args.data_parallel:
pol.dp_run = False
vf.dp_run = False
total_epi += traj.num_epi
step = traj.num_step
total_step += step
rewards = [np.sum(epi['rews']) for epi in epis]
