def _reconstruct_img(self, flat_img):
latent_distribution_params = self.vae.encode(
ptu.from_numpy(flat_img.reshape(1, -1)))
reconstructions, _ = self.vae.decode(latent_distribution_params[0])
imgs = ptu.get_numpy(reconstructions)
imgs = imgs.reshape(1, self.input_channels, self.imsize, self.imsize)
return imgs[0]
def _update_info(self, info, obs):
latent_distribution_params = self.vae.encode(
ptu.from_numpy(obs[self.vae_input_observation_key].reshape(1, -1)))
latent_obs, logvar = ptu.get_numpy(
latent_distribution_params[0])[0], ptu.get_numpy(
latent_distribution_params[1])[0]
# assert (latent_obs == obs['latent_observation']).all()
latent_goal = self.desired_goal['latent_desired_goal']
dist = latent_goal - latent_obs
var = np.exp(logvar.flatten())
var = np.maximum(var, self.reward_min_variance)
err = dist * dist / 2 / var
mdist = np.sum(err) # mahalanobis distance
info["vae_mdist"] = mdist
info["vae_success"] = 1 if mdist < self.epsilon else 0
info["vae_dist"] = np.linalg.norm(dist, ord=self.norm_order)
info["vae_dist_l1"] = np.linalg.norm(dist, ord=1)
info["vae_dist_l2"] = np.linalg.norm(dist, ord=2)
def train_from_torch(self, batch):
rewards = batch['rewards']
terminals = batch['terminals']
obs = batch['observations']
actions = batch['actions']
next_obs = batch['next_observations']
p_z = self.sample_empowerment_latents(obs)
"""
Update the networks
"""
rews = np.mean(ptu.get_numpy(rewards))
vf_loss, alpha_loss, alpha, qf1_loss, qf2_loss, emp_reward, pol_loss = self.update_state_value(
observation=obs,
action=actions,
done=terminals,
next_observation=next_obs,
p_z_given=p_z)
# Update the discriminator
discriminator_loss = self.update_discriminator(observation=obs,
action=actions)
disc_loss = np.mean(ptu.get_numpy(discriminator_loss))
pol = np.mean(ptu.get_numpy(pol_loss))
emp_rew = np.mean(ptu.get_numpy(emp_reward))
value_loss = np.mean(ptu.get_numpy(vf_loss))
q1_loss = np.mean(ptu.get_numpy(qf1_loss))
q2_loss = np.mean(ptu.get_numpy(qf2_loss))
i = self._n_train_steps_total
self.writer.add_scalar('data/reward', rews, i)
self.writer.add_scalar('data/policy_loss', pol, i)
self.writer.add_scalar('data/discriminator_loss', disc_loss, i)
self.writer.add_scalar('data/empowerment_rewards', emp_rew, i)
self.writer.add_scalar('data/value_loss', value_loss, i)
self.writer.add_scalar('data/q_value_loss_1', q1_loss, i)
self.writer.add_scalar('data/q_value_loss_2', q2_loss, i)
self._n_train_steps_total += 1
def np_ify(tensor_or_other):
if isinstance(tensor_or_other, torch.autograd.Variable):
return ptu.get_numpy(tensor_or_other)
else:
return tensor_or_other
def _do_training(self):
batch = self.get_batch()
rewards = batch['rewards']
terminals = batch['terminals']
obs = batch['observations']
actions = batch['actions']
next_obs = batch['next_observations']
q1_pred = self.qf1(obs, actions)
q2_pred = self.qf2(obs, actions)
v_pred = self.vf(obs)
# Make sure policy accounts for squashing functions like tanh correctly!
policy_outputs = self.policy(obs,
reparameterize=self.train_policy_with_reparameterization,
return_log_prob=True)
new_actions, policy_mean, policy_log_std, log_pi = policy_outputs[:4]
"""
Alpha Loss (if applicable)
"""
if self.use_automatic_entropy_tuning:
"""
Alpha Loss
"""
alpha_loss = -(self.log_alpha * (log_pi +
self.target_entropy).detach()).mean()
self.alpha_optimizer.zero_grad()
alpha_loss.backward()
self.alpha_optimizer.step()
alpha = self.log_alpha.exp()
else:
alpha = 1
alpha_loss = 0
"""
QF Loss
"""
target_v_values = self.target_vf(next_obs)
q_target = self.reward_scale * rewards + \
(1. - terminals) * self.discount * target_v_values
qf1_loss = self.qf_criterion(q1_pred, q_target.detach())
qf2_loss = self.qf_criterion(q2_pred, q_target.detach())
"""
VF Loss
"""
q_new_actions = torch.min(
self.qf1(obs, new_actions),
self.qf2(obs, new_actions),
)
v_target = q_new_actions - alpha*log_pi
vf_loss = self.vf_criterion(v_pred, v_target.detach())
"""
Update networks
"""
self.qf1_optimizer.zero_grad()
qf1_loss.backward()
self.qf1_optimizer.step()
self.qf2_optimizer.zero_grad()
qf2_loss.backward()
self.qf2_optimizer.step()
self.vf_optimizer.zero_grad()
vf_loss.backward()
self.vf_optimizer.step()
policy_loss = None
if self._n_train_steps_total % self.policy_update_period == 0:
"""
Policy Loss
"""
if self.train_policy_with_reparameterization:
policy_loss = (alpha*log_pi - q_new_actions).mean()
else:
log_policy_target = q_new_actions - v_pred
policy_loss = (
log_pi * (alpha*log_pi - log_policy_target).detach()
).mean()
mean_reg_loss = self.policy_mean_reg_weight * \
(policy_mean**2).mean()
std_reg_loss = self.policy_std_reg_weight * \
(policy_log_std**2).mean()
pre_tanh_value = policy_outputs[-1]
pre_activation_reg_loss = self.policy_pre_activation_weight * (
(pre_tanh_value**2).sum(dim=1).mean()
)
policy_reg_loss = mean_reg_loss + std_reg_loss + pre_activation_reg_loss
policy_loss = policy_loss + policy_reg_loss
self.policy_optimizer.zero_grad()
policy_loss.backward()
self.policy_optimizer.step()
if self._n_train_steps_total % self.target_update_period == 0:
ptu.soft_update_from_to(
self.vf, self.target_vf, self.soft_target_tau
)
"""
Save some statistics for eval using just one batch.
"""
if self.need_to_update_eval_statistics:
self.need_to_update_eval_statistics = False
if policy_loss is None:
if self.train_policy_with_reparameterization:
policy_loss = (log_pi - q_new_actions).mean()
else:
log_policy_target = q_new_actions - v_pred
policy_loss = (
log_pi * (log_pi - log_policy_target).detach()
).mean()
mean_reg_loss = self.policy_mean_reg_weight * \
(policy_mean**2).mean()
std_reg_loss = self.policy_std_reg_weight * \
(policy_log_std**2).mean()
pre_tanh_value = policy_outputs[-1]
pre_activation_reg_loss = self.policy_pre_activation_weight * (
(pre_tanh_value**2).sum(dim=1).mean()
)
policy_reg_loss = mean_reg_loss + std_reg_loss + pre_activation_reg_loss
policy_loss = policy_loss + policy_reg_loss
self.eval_statistics['QF1 Loss'] = np.mean(ptu.get_numpy(qf1_loss))
self.eval_statistics['QF2 Loss'] = np.mean(ptu.get_numpy(qf2_loss))
self.eval_statistics['VF Loss'] = np.mean(ptu.get_numpy(vf_loss))
self.eval_statistics['Policy Loss'] = np.mean(ptu.get_numpy(
policy_loss
))
self.eval_statistics.update(create_stats_ordered_dict(
'Q1 Predictions',
ptu.get_numpy(q1_pred),
))
self.eval_statistics.update(create_stats_ordered_dict(
'Q2 Predictions',
ptu.get_numpy(q2_pred),
))
self.eval_statistics.update(create_stats_ordered_dict(
'V Predictions',
ptu.get_numpy(v_pred),
))
self.eval_statistics.update(create_stats_ordered_dict(
'Log Pis',
ptu.get_numpy(log_pi),
))
self.eval_statistics.update(create_stats_ordered_dict(
'Policy mu',
ptu.get_numpy(policy_mean),
))
self.eval_statistics.update(create_stats_ordered_dict(
'Policy log std',
ptu.get_numpy(policy_log_std),
))
if self.use_automatic_entropy_tuning:
self.eval_statistics['Alpha'] = alpha.item()
self.eval_statistics['Alpha Loss'] = alpha_loss.item()
def _encode(self, imgs):
latent_distribution_params = self.vae.encode(ptu.from_numpy(imgs))
return ptu.get_numpy(latent_distribution_params[0])
def _decode(self, latents):
reconstructions, _ = self.vae.decode(ptu.from_numpy(latents))
decoded = ptu.get_numpy(reconstructions)
return decoded
def update_state_value(self, observation, action, p_z_given,
next_observation, done):
"""
Creates minimization operations for the state value functions.
In principle, there is no need for a separate state value function
approximator, since it could be evaluated using the Q-function and
policy. However, in practice, the separate function approximator
stabilizes training.
:return:
"""
qf1_loss, qf2_loss, empowerment_reward, _ = self.update_critic(
observation=observation,
action=action,
p_z_given=p_z_given,
next_observation=next_observation,
done=done)
# Make sure policy accounts for squashing functions like tanh correctly!
policy_outputs = self.policy(
observation,
reparameterize=self.train_policy_with_reparameterization,
return_log_prob=True)
new_actions, policy_mean, policy_log_std, log_pi = policy_outputs[:4]
q_new_actions = torch.min(
self.qf1(observation, new_actions),
self.qf2(observation, new_actions),
)
(observation, z_one_hot) = self.split_obs(obs=observation)
v_pred = self.value_network(observation)
"""
Alpha Loss (if applicable)
"""
if self.use_automatic_entropy_tuning:
"""
Alpha Loss
"""
alpha_loss = -(self.log_alpha *
(log_pi + self.target_entropy).detach()).mean()
self.alpha_optimizer.zero_grad()
alpha_loss.backward()
self.alpha_optimizer.step()
alpha = self.log_alpha.exp()
else:
alpha = 1
alpha_loss = 0
policy_distribution = self.policy.get_distibution(observation)
log_pi = policy_distribution.log_pi
v_target = q_new_actions - alpha * log_pi
vf_loss = self.vf_criterion(v_pred, v_target.detach())
"""
Update networks
"""
self.qf1_optimizer.zero_grad()
qf1_loss.backward()
self.qf1_optimizer.step()
self.qf2_optimizer.zero_grad()
qf2_loss.backward()
self.qf2_optimizer.step()
self.vf_optimizer.zero_grad()
vf_loss.backward()
self.vf_optimizer.step()
policy_loss = None
if self._n_train_steps_total % self.policy_update_period == 0:
"""
Policy Loss
"""
if self.train_policy_with_reparameterization:
policy_loss = (alpha * log_pi - q_new_actions).mean()
else:
log_policy_target = q_new_actions - v_pred
policy_loss = (
log_pi *
(alpha * log_pi - log_policy_target).detach()).mean()
mean_reg_loss = self.policy_mean_reg_weight * (policy_mean**
2).mean()
std_reg_loss = self.policy_std_reg_weight * (policy_log_std**
2).mean()
pre_tanh_value = policy_outputs[-1]
pre_activation_reg_loss = self.policy_pre_activation_weight * (
(pre_tanh_value**2).sum(dim=1).mean())
policy_reg_loss = mean_reg_loss + std_reg_loss + pre_activation_reg_loss
policy_loss = policy_loss + policy_reg_loss
self.policy_optimizer.zero_grad()
policy_loss.backward()
self.policy_optimizer.step()
if self._n_train_steps_total % self.target_update_period == 0:
ptu.soft_update_from_to(self.value_network, self.target_vf,
self.soft_target_tau)
"""
Save some statistics for eval using just one batch.
"""
if self.need_to_update_eval_statistics:
self.need_to_update_eval_statistics = False
if policy_loss is None:
if self.train_policy_with_reparameterization:
policy_loss = (log_pi - q_new_actions).mean()
else:
log_policy_target = q_new_actions - v_pred
policy_loss = (
log_pi * (log_pi - log_policy_target).detach()).mean()
mean_reg_loss = self.policy_mean_reg_weight * (policy_mean**
2).mean()
std_reg_loss = self.policy_std_reg_weight * (policy_log_std**
2).mean()
pre_tanh_value = policy_outputs[-1]
pre_activation_reg_loss = self.policy_pre_activation_weight * (
(pre_tanh_value**2).sum(dim=1).mean())
policy_reg_loss = mean_reg_loss + std_reg_loss + pre_activation_reg_loss
policy_loss = policy_loss + policy_reg_loss
self.eval_statistics['QF1 Loss'] = np.mean(ptu.get_numpy(qf1_loss))
self.eval_statistics['QF2 Loss'] = np.mean(ptu.get_numpy(qf2_loss))
self.eval_statistics['VF Loss'] = np.mean(ptu.get_numpy(vf_loss))
self.eval_statistics['Policy Loss'] = np.mean(
ptu.get_numpy(policy_loss))
self.eval_statistics.update(
create_stats_ordered_dict(
'V Predictions',
ptu.get_numpy(v_pred),
))
self.eval_statistics.update(
create_stats_ordered_dict(
'Log Pis',
ptu.get_numpy(log_pi),
))
self.eval_statistics.update(
create_stats_ordered_dict(
'Policy mu',
ptu.get_numpy(policy_mean),
))
self.eval_statistics.update(
create_stats_ordered_dict(
'Policy log std',
ptu.get_numpy(policy_log_std),
))
self.eval_statistics.update(
create_stats_ordered_dict(
'Empowerment Reward',
ptu.get_numpy(empowerment_reward),
))
if self.use_automatic_entropy_tuning:
self.eval_statistics['Alpha'] = alpha.item()
self.eval_statistics['Alpha Loss'] = alpha_loss.item()
return vf_loss, alpha_loss, alpha, qf1_loss, qf2_loss, empowerment_reward, policy_loss
def train_from_torch(self, batch):
rewards = batch['rewards']
terminals = batch['terminals']
obs = batch['observations']
actions = batch['actions']
next_obs = batch['next_observations']
"""
Policy and Alpha Loss
"""
new_obs_actions, policy_mean, policy_log_std, log_pi, *_ = self.policy(
obs, reparameterize=True, return_log_prob=True,
)
if self.use_automatic_entropy_tuning:
alpha_loss = -(self.log_alpha * (log_pi + self.target_entropy).detach()).mean()
self.alpha_optimizer.zero_grad()
alpha_loss.backward()
self.alpha_optimizer.step()
alpha = self.log_alpha.exp()
else:
alpha_loss = 0
alpha = 1
q_new_actions = torch.min(
self.qf1(obs, new_obs_actions),
self.qf2(obs, new_obs_actions),
)
policy_loss = (alpha*log_pi - q_new_actions).mean()
"""
QF Loss
"""
q1_pred = self.qf1(obs, actions)
q2_pred = self.qf2(obs, actions)
# Make sure policy accounts for squashing functions like tanh correctly!
new_next_actions, _, _, new_log_pi, *_ = self.policy(
next_obs, reparameterize=True, return_log_prob=True,
)
target_q_values = torch.min(
self.target_qf1(next_obs, new_next_actions),
self.target_qf2(next_obs, new_next_actions),
) - alpha * new_log_pi
q_target = self.reward_scale * rewards + (1. - terminals) * self.discount * target_q_values
qf1_loss = self.qf_criterion(q1_pred, q_target.detach())
qf2_loss = self.qf_criterion(q2_pred, q_target.detach())
"""
Update networks
"""
self.qf1_optimizer.zero_grad()
qf1_loss.backward()
self.qf1_optimizer.step()
self.qf2_optimizer.zero_grad()
qf2_loss.backward()
self.qf2_optimizer.step()
self.policy_optimizer.zero_grad()
policy_loss.backward()
self.policy_optimizer.step()
"""
Soft Updates
"""
if self._n_train_steps_total % self.target_update_period == 0:
ptu.soft_update_from_to(
self.qf1, self.target_qf1, self.soft_target_tau
)
ptu.soft_update_from_to(
self.qf2, self.target_qf2, self.soft_target_tau
)
"""
Save some statistics for eval
"""
if self._need_to_update_eval_statistics:
self._need_to_update_eval_statistics = False
"""
Eval should set this to None.
This way, these statistics are only computed for one batch.
"""
policy_loss = (log_pi - q_new_actions).mean()
self.eval_statistics['QF1 Loss'] = np.mean(ptu.get_numpy(qf1_loss))
self.eval_statistics['QF2 Loss'] = np.mean(ptu.get_numpy(qf2_loss))
self.eval_statistics['Policy Loss'] = np.mean(ptu.get_numpy(
policy_loss
))
self.eval_statistics.update(create_stats_ordered_dict(
'Q1 Predictions',
ptu.get_numpy(q1_pred),
))
self.eval_statistics.update(create_stats_ordered_dict(
'Q2 Predictions',
ptu.get_numpy(q2_pred),
))
self.eval_statistics.update(create_stats_ordered_dict(
'Q Targets',
ptu.get_numpy(q_target),
))
self.eval_statistics.update(create_stats_ordered_dict(
'Log Pis',
ptu.get_numpy(log_pi),
))
self.eval_statistics.update(create_stats_ordered_dict(
'Policy mu',
ptu.get_numpy(policy_mean),
))
self.eval_statistics.update(create_stats_ordered_dict(
'Policy log std',
ptu.get_numpy(policy_log_std),
))
if self.use_automatic_entropy_tuning:
self.eval_statistics['Alpha'] = alpha.item()
self.eval_statistics['Alpha Loss'] = alpha_loss.item()
self._n_train_steps_total += 1
