def train_from_torch(self, batch):
rewards = batch['rewards']
terminals = batch['terminals']
obs = batch['observations']
actions = batch['actions']
next_obs = batch['next_observations']
batch_size = obs.size(0)
"""
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_all, _, _ = self.qf(obs, new_obs_actions)
q_all = q_all.view(batch_size, self.num_samples, 1)
q_new_actions, _ = torch.min(q_all, dim=1)
policy_loss = (alpha * log_pi - q_new_actions).mean()
"""
QF Loss
"""
q_pred, mu, std = self.qf(obs, actions)
print(q_pred)
# 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_all, _, _ = self.target_qf(next_obs, new_next_actions)
target_all = target_all.view(self.num_samples, batch_size, 1)
print(target_all)
print(target_all.size())
target_q_values, _ = torch.min(target_all, dim=1)
print(target_q_values)
target_q_values = target_q_values - alpha * new_log_pi
q_target = self.reward_scale * rewards + (
1. - terminals) * self.discount * target_q_values
if self.weighted_mse:
raise NotImplementedError
else:
q_target = q_target.repeat_interleave(self.num_samples, dim=0)
qf_loss = self.qf_criterion(q_pred, q_target.detach())
qf_loss += self.beta * kl_divergence(mu, std)
"""
Update networks
"""
self.qf_optimizer.zero_grad()
qf_loss.backward()
self.qf_optimizer.step()
self.policy_optimizer.zero_grad()
policy_loss.backward()
self.policy_optimizer.step()
# exit()
"""
Soft Updates
"""
if self._n_train_steps_total % self.target_update_period == 0:
ptu.soft_update_from_to(self.qf, self.target_qf,
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['QF Loss'] = np.mean(ptu.get_numpy(qf_loss))
self.eval_statistics['Policy Loss'] = np.mean(
ptu.get_numpy(policy_loss))
self.eval_statistics.update(
create_stats_ordered_dict(
'Q Predictions',
ptu.get_numpy(q_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
def train_from_torch(self, batch):
rewards = batch['rewards']
terminals = batch['terminals']
obs = batch['observations']
actions = batch['actions']
next_obs = batch['next_observations']
# make rewrads posutive
if self.rewards_shift_param is not None:
rewards = rewards - self.rewards_shift_param
"""
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(retain_graph=True)
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),
)
# use rnd in policy
if self.use_rnd_policy:
actor_bonus = self._get_bonus(obs, new_obs_actions)
q_new_actions = q_new_actions - self.beta * actor_bonus
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
# use rnd in critic
if self.use_rnd_critic:
critic_bonus = self._get_bonus(next_obs, new_next_actions)
target_q_values = target_q_values - self.beta * critic_bonus
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.policy_optimizer.zero_grad()
policy_loss.backward()
self.policy_optimizer.step()
self.qf1_optimizer.zero_grad()
qf1_loss.backward()
self.qf1_optimizer.step()
self.qf2_optimizer.zero_grad()
qf2_loss.backward()
self.qf2_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
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 = self.fixed_entropy
alpha_loss = 0
"""
QF Loss
"""
target_v_values = self.target_vf(next_obs)
q_target = self.reward_scale * rewards.squeeze_().unsqueeze_(-1) + (
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 train_from_torch(self, batch):
rewards_n = batch['rewards'].detach()
terminals_n = batch['terminals'].detach()
obs_n = batch['observations'].detach()
actions_n = batch['actions'].detach()
next_obs_n = batch['next_observations'].detach()
batch_size = rewards_n.shape[0]
num_agent = rewards_n.shape[1]
whole_obs = obs_n.view(batch_size, -1)
whole_actions = actions_n.view(batch_size, -1)
whole_next_obs = next_obs_n.view(batch_size, -1)
"""
Policy operations.
"""
online_actions_n, online_pre_values_n, online_log_pis_n = [], [], []
for agent in range(num_agent):
policy_actions, info = self.policy_n[agent](
obs_n[:,agent,:], return_info=True,
)
online_actions_n.append(policy_actions)
online_pre_values_n.append(info['preactivation'])
online_log_pis_n.append(info['log_prob'])
k0_actions = torch.stack(online_actions_n) # num_agent x batch x a_dim
k0_actions = k0_actions.transpose(0,1).contiguous() # batch x num_agent x a_dim
k0_inputs = torch.cat([obs_n, k0_actions],dim=-1)
k1_actions = self.cactor(k0_inputs, deterministic=self.deterministic_cactor_in_graph)
k1_inputs = torch.cat([obs_n, k1_actions],dim=-1)
k1_contexts_1 = self.cg1(k1_inputs)
k1_contexts_2 = self.cg2(k1_inputs)
policy_gradients_n = []
alpha_n = []
for agent in range(num_agent):
policy_actions = online_actions_n[agent]
pre_value = online_pre_values_n[agent]
log_pi = online_log_pis_n[agent]
if self.pre_activation_weight > 0.:
pre_activation_policy_loss = (
(pre_value**2).sum(dim=1).mean()
)
else:
pre_activation_policy_loss = torch.tensor(0.).to(ptu.device)
if self.use_entropy_loss:
if self.use_automatic_entropy_tuning:
if self.state_dependent_alpha:
alpha = self.log_alpha_n[agent](obs_n[:,agent,:]).exp()
else:
alpha = self.log_alpha_n[agent].exp()
alpha_loss = -(alpha * (log_pi + self.target_entropy).detach()).mean()
self.alpha_optimizer_n[agent].zero_grad()
alpha_loss.backward()
self.alpha_optimizer_n[agent].step()
if self.state_dependent_alpha:
alpha = self.log_alpha_n[agent](obs_n[:,agent,:]).exp().detach()
else:
alpha = self.log_alpha_n[agent].exp().detach()
alpha_n.append(alpha)
else:
alpha_loss = torch.tensor(0.).to(ptu.device)
alpha = torch.tensor(self.init_alpha).to(ptu.device)
alpha_n.append(alpha)
entropy_loss = (alpha*log_pi).mean()
else:
entropy_loss = torch.tensor(0.).to(ptu.device)
q1_input = torch.cat([policy_actions,k1_contexts_1[:,agent,:]],dim=-1)
q1_output = self.qf1(q1_input)
q2_input = torch.cat([policy_actions,k1_contexts_2[:,agent,:]],dim=-1)
q2_output = self.qf2(q2_input)
q_output = torch.min(q1_output,q2_output)
raw_policy_loss = -q_output.mean()
policy_loss = (
raw_policy_loss +
pre_activation_policy_loss * self.pre_activation_weight +
entropy_loss
)
policy_gradients_n.append(torch.autograd.grad(policy_loss, self.policy_n[agent].parameters(),retain_graph=True))
if self._need_to_update_eval_statistics:
self.eval_statistics['Policy Loss {}'.format(agent)] = np.mean(ptu.get_numpy(
policy_loss
))
self.eval_statistics['Raw Policy Loss {}'.format(agent)] = np.mean(ptu.get_numpy(
raw_policy_loss
))
self.eval_statistics['Preactivation Policy Loss {}'.format(agent)] = np.mean(ptu.get_numpy(
pre_activation_policy_loss
))
self.eval_statistics['Entropy Loss {}'.format(agent)] = np.mean(ptu.get_numpy(
entropy_loss
))
if self.use_entropy_loss:
if self.state_dependent_alpha:
self.eval_statistics.update(create_stats_ordered_dict(
'Alpha {}'.format(agent),
ptu.get_numpy(alpha),
))
else:
self.eval_statistics['Alpha {} Mean'.format(agent)] = np.mean(ptu.get_numpy(
alpha
))
self.eval_statistics.update(create_stats_ordered_dict(
'Policy Action {}'.format(agent),
ptu.get_numpy(policy_actions),
))
for agent in range(num_agent):
# self.policy_optimizer_n[agent].zero_grad()
for pid,p in enumerate(self.policy_n[agent].parameters()):
p.grad = policy_gradients_n[agent][pid]
self.policy_optimizer_n[agent].step()
"""
Critic operations.
"""
with torch.no_grad():
next_actions_n, next_log_pis_n = [], []
for agent in range(num_agent):
next_actions, next_info = self.policy_n[agent](
next_obs_n[:,agent,:], return_info=True,
deterministic=self.deterministic_next_action,
)
next_actions_n.append(next_actions)
next_log_pis_n.append(next_info['log_prob'])
next_k0_actions = torch.stack(next_actions_n) # num_agent x batch x a_dim
next_k0_actions = next_k0_actions.transpose(0,1).contiguous() # batch x num_agent x a_dim
next_k0_inputs = torch.cat([next_obs_n, next_k0_actions],dim=-1)
next_k1_actions = self.cactor(next_k0_inputs, deterministic=self.deterministic_cactor_in_graph)
next_k1_inputs = torch.cat([next_obs_n, next_k1_actions],dim=-1)
next_k1_contexts_1 = self.cg1(next_k1_inputs)
next_k1_contexts_2 = self.cg2(next_k1_inputs)
buffer_inputs = torch.cat([obs_n, actions_n],dim=-1)
buffer_contexts_1 = self.cg1(buffer_inputs) # batch x num_agent x c_dim
q1_inputs = torch.cat([actions_n, buffer_contexts_1],dim=-1)
q1_preds_n = self.qf1(q1_inputs)
buffer_contexts_2 = self.cg2(buffer_inputs) # batch x num_agent x c_dim
q2_inputs = torch.cat([actions_n, buffer_contexts_2],dim=-1)
q2_preds_n = self.qf2(q2_inputs)
raw_qf1_loss_n, raw_qf2_loss_n, q_target_n = [], [], []
for agent in range(num_agent):
with torch.no_grad():
next_policy_actions = next_actions_n[agent]
next_log_pi = next_log_pis_n[agent]
next_q1_input = torch.cat([next_policy_actions,next_k1_contexts_1[:,agent,:]],dim=-1)
next_target_q1_values = self.target_qf1(next_q1_input)
next_q2_input = torch.cat([next_policy_actions,next_k1_contexts_2[:,agent,:]],dim=-1)
next_target_q2_values = self.target_qf2(next_q2_input)
next_target_q_values = torch.min(next_target_q1_values, next_target_q2_values)
if self.use_entropy_reward:
if self.state_dependent_alpha:
next_alpha = self.log_alpha_n[agent](next_obs_n[:,agent,:]).exp()
else:
next_alpha = alpha_n[agent]
next_target_q_values = next_target_q_values - next_alpha * next_log_pi
q_target = self.reward_scale*rewards_n[:,agent,:] + (1. - terminals_n[:,agent,:]) * self.discount * next_target_q_values
q_target = torch.clamp(q_target, self.min_q_value, self.max_q_value)
q_target_n.append(q_target)
q1_pred = q1_preds_n[:,agent,:]
raw_qf1_loss = self.qf_criterion(q1_pred, q_target)
raw_qf1_loss_n.append(raw_qf1_loss)
q2_pred = q2_preds_n[:,agent,:]
raw_qf2_loss = self.qf_criterion(q2_pred, q_target)
raw_qf2_loss_n.append(raw_qf2_loss)
if self._need_to_update_eval_statistics:
self.eval_statistics['QF1 Loss {}'.format(agent)] = np.mean(ptu.get_numpy(raw_qf1_loss))
self.eval_statistics['QF2 Loss {}'.format(agent)] = np.mean(ptu.get_numpy(raw_qf2_loss))
self.eval_statistics.update(create_stats_ordered_dict(
'Q1 Predictions {}'.format(agent),
ptu.get_numpy(q1_pred),
))
self.eval_statistics.update(create_stats_ordered_dict(
'Q2 Predictions {}'.format(agent),
ptu.get_numpy(q2_pred),
))
self.eval_statistics.update(create_stats_ordered_dict(
'Q Targets {}'.format(agent),
ptu.get_numpy(q_target),
))
if self.sum_n_loss:
raw_qf1_loss = torch.sum(torch.stack(raw_qf1_loss_n))
raw_qf2_loss = torch.sum(torch.stack(raw_qf2_loss_n))
else:
raw_qf1_loss = torch.mean(torch.stack(raw_qf1_loss_n))
raw_qf2_loss = torch.mean(torch.stack(raw_qf2_loss_n))
if self.negative_sampling:
perturb_actions = actions_n.clone() # batch x agent x |A|
batch_size, num_agent, a_dim = perturb_actions.shape
perturb_agents = torch.randint(low=0,high=num_agent,size=(batch_size,))
neg_actions = torch.rand(batch_size,a_dim)*2.-1. # ranged in -1 to 1
perturb_actions[torch.arange(batch_size),perturb_agents,:] = neg_actions
perturb_inputs = torch.cat([obs_n,perturb_actions],dim=-1)
perturb_contexts_1 = self.cg1(perturb_inputs) # batch x num_agent x c_dim
perturb_q1_inputs = torch.cat([actions_n, perturb_contexts_1],dim=-1)
perturb_q1_preds = self.qf1(perturb_q1_inputs)[torch.arange(batch_size),perturb_agents,:]
perturb_contexts_2 = self.cg2(perturb_inputs) # batch x num_agent x c_dim
perturb_q2_inputs = torch.cat([actions_n, perturb_contexts_2],dim=-1)
perturb_q2_preds = self.qf2(perturb_q2_inputs)[torch.arange(batch_size),perturb_agents,:]
perturb_q_targets = torch.stack(q_target_n).transpose(0,1).contiguous()[torch.arange(batch_size),perturb_agents,:]
neg_loss1 = self.qf_criterion(perturb_q1_preds, perturb_q_targets)
neg_loss2 = self.qf_criterion(perturb_q2_preds, perturb_q_targets)
else:
neg_loss1, neg_loss2 = torch.tensor(0.).to(ptu.device), torch.tensor(0.).to(ptu.device)
if self.qf_weight_decay > 0:
reg_loss1 = self.qf_weight_decay * sum(
torch.sum(param ** 2)
for param in list(self.qf1.regularizable_parameters())+list(self.cg1.regularizable_parameters())
)
reg_loss2 = self.qf_weight_decay * sum(
torch.sum(param ** 2)
for param in list(self.qf2.regularizable_parameters())+list(stack.cg2.regularizable_parameters())
)
else:
reg_loss1, reg_loss2 = torch.tensor(0.).to(ptu.device), torch.tensor(0.).to(ptu.device)
qf1_loss = raw_qf1_loss + reg_loss1 + neg_loss1
qf2_loss = raw_qf2_loss + reg_loss2 + neg_loss2
if self._need_to_update_eval_statistics:
self.eval_statistics['raw_qf1_loss'] = np.mean(ptu.get_numpy(raw_qf1_loss))
self.eval_statistics['raw_qf2_loss'] = np.mean(ptu.get_numpy(raw_qf2_loss))
self.eval_statistics['neg_qf1_loss'] = np.mean(ptu.get_numpy(neg_loss1))
self.eval_statistics['neg_qf2_loss'] = np.mean(ptu.get_numpy(neg_loss2))
self.eval_statistics['reg_qf2_loss'] = np.mean(ptu.get_numpy(reg_loss1))
self.eval_statistics['reg_qf2_loss'] = np.mean(ptu.get_numpy(reg_loss2))
self.qf1_optimizer.zero_grad()
qf1_loss.backward()
self.qf1_optimizer.step()
self.qf2_optimizer.zero_grad()
qf2_loss.backward()
self.qf2_optimizer.step()
"""
Central actor operations.
"""
buffer_inputs = torch.cat([obs_n, actions_n],dim=-1)
cactor_actions, cactor_infos = self.cactor(buffer_inputs,return_info=True)
# batch x agent_num x |A|
buffer_contexts_1 = self.cg1(buffer_inputs)
buffer_contexts_2 = self.cg2(buffer_inputs)
cactor_loss_n = []
for agent in range(num_agent):
cactor_pre_value = cactor_infos['preactivation'][:,agent,:]
if self.pre_activation_weight > 0:
pre_activation_cactor_loss = (
(cactor_pre_value**2).sum(dim=1).mean()
)
else:
pre_activation_cactor_loss = torch.tensor(0.).to(ptu.device)
if self.use_cactor_entropy_loss:
cactor_log_pi = cactor_infos['log_prob'][:,agent,:]
if self.use_automatic_entropy_tuning:
if self.state_dependent_alpha:
calpha = self.log_calpha_n[agent](whole_obs).exp()
else:
calpha = self.log_calpha_n[agent].exp()
calpha_loss = -(calpha * (cactor_log_pi + self.target_entropy).detach()).mean()
self.calpha_optimizer_n[agent].zero_grad()
calpha_loss.backward()
self.calpha_optimizer_n[agent].step()
if self.state_dependent_alpha:
calpha = self.log_calpha_n[agent](whole_obs).exp().detach()
else:
calpha = self.log_calpha_n[agent].exp().detach()
else:
calpha_loss = torch.tensor(0.).to(ptu.device)
calpha = torch.tensor(self.init_alpha).to(ptu.device)
cactor_entropy_loss = (calpha*cactor_log_pi).mean()
else:
cactor_entropy_loss = torch.tensor(0.).to(ptu.device)
q1_input = torch.cat([cactor_actions[:,agent,:],buffer_contexts_1[:,agent,:]],dim=-1)
q1_output = self.qf1(q1_input)
q2_input = torch.cat([cactor_actions[:,agent,:],buffer_contexts_2[:,agent,:]],dim=-1)
q2_output = self.qf2(q2_input)
q_output = torch.min(q1_output,q2_output)
raw_cactor_loss = -q_output.mean()
cactor_loss = (
raw_cactor_loss +
pre_activation_cactor_loss * self.pre_activation_weight +
cactor_entropy_loss
)
cactor_loss_n.append(cactor_loss)
if self._need_to_update_eval_statistics:
if self.use_cactor_entropy_loss:
if self.state_dependent_alpha:
self.eval_statistics.update(create_stats_ordered_dict(
'CAlpha {}'.format(agent),
ptu.get_numpy(calpha),
))
else:
self.eval_statistics['CAlpha {} Mean'.format(agent)] = np.mean(ptu.get_numpy(
calpha
))
self.eval_statistics['Cactor Loss {}'.format(agent)] = np.mean(ptu.get_numpy(
cactor_loss
))
self.eval_statistics['Raw Cactor Loss {}'.format(agent)] = np.mean(ptu.get_numpy(
raw_cactor_loss
))
self.eval_statistics['Preactivation Cactor Loss {}'.format(agent)] = np.mean(ptu.get_numpy(
pre_activation_cactor_loss
))
self.eval_statistics['Entropy Cactor Loss {}'.format(agent)] = np.mean(ptu.get_numpy(
cactor_entropy_loss
))
if self.sum_n_loss:
cactor_loss = torch.sum(torch.stack(cactor_loss_n))
else:
cactor_loss = torch.mean(torch.stack(cactor_loss_n))
self.cactor_optimizer.zero_grad()
cactor_loss.backward()
self.cactor_optimizer.step()
self._need_to_update_eval_statistics = False
self._update_target_networks()
self._n_train_steps_total += 1
def train_from_torch(self, batch):
obs = batch['observations']
old_log_pi = batch['log_prob']
advantage = batch['advantage']
returns = batch['returns']
actions = batch['actions']
"""
Policy Loss
"""
_, _, all_probs = self.policy(obs)
new_log_pi = torch.zeros(all_probs.shape[0], 1)
for i in range(all_probs.shape[0]):
new_log_pi[i] = Categorical(all_probs[i]).log_prob(actions[i]).sum()
# Advantage Clip
ratio = torch.exp(new_log_pi - old_log_pi)
left = ratio * advantage
right = torch.clamp(ratio, 1 - self.epsilon, 1 + self.epsilon) * advantage
policy_loss = (-1 * torch.min(left, right)).mean()
"""
VF Loss
"""
v_pred = self.vf(obs)
v_target = returns
vf_loss = self.vf_criterion(v_pred, v_target)
"""
Update networks
"""
loss = policy_loss + vf_loss
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
if self.last_approx_kl is None or not self._need_to_update_eval_statistics:
self.last_approx_kl = (old_log_pi - new_log_pi).detach()
approx_ent = -new_log_pi
"""
Save some statistics for eval
"""
if self._need_to_update_eval_statistics:
policy_grads = torch.cat([p.grad.flatten() for p in self.policy.parameters()])
value_grads = torch.cat([p.grad.flatten() for p in self.vf.parameters()])
self._need_to_update_eval_statistics = False
"""
Eval should set this to None.
This way, these statistics are only computed for one batch.
"""
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(
'V Target',
ptu.get_numpy(v_target),
))
self.eval_statistics.update(create_stats_ordered_dict(
'Policy Gradients',
ptu.get_numpy(policy_grads),
))
self.eval_statistics.update(create_stats_ordered_dict(
'Value Gradients',
ptu.get_numpy(value_grads),
))
self.eval_statistics.update(create_stats_ordered_dict(
'Policy KL',
ptu.get_numpy(self.last_approx_kl),
))
self.eval_statistics.update(create_stats_ordered_dict(
'Policy Entropy',
ptu.get_numpy(approx_ent),
))
self.eval_statistics.update(create_stats_ordered_dict(
'New Log Pis',
ptu.get_numpy(new_log_pi),
))
self.eval_statistics.update(create_stats_ordered_dict(
'Old Log Pis',
ptu.get_numpy(old_log_pi),
))
self._n_train_steps_total += 1
def get_param_values_np(self):
state_dict = self.state_dict()
np_dict = OrderedDict()
for key, tensor in state_dict.items():
np_dict[key] = ptu.get_numpy(tensor)
return np_dict
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']
"""
Critic operations.
"""
# add some gaussian noise
next_actions = self.target_policy(next_obs)
noise = torch.normal(
torch.zeros_like(next_actions),
self.target_policy_noise,
)
noise = torch.clamp(noise, -self.target_policy_noise_clip,
self.target_policy_noise_clip)
noisy_next_actions = next_actions + noise
bellman_errors = []
if self.stop_critic_training is None or self.stop_critic_training >= self.current_epoch:
for idx in range(self.num_q):
target_q_values = self.target_qfs[idx](next_obs,
noisy_next_actions)
q_target = rewards + (
1. - terminals) * self.discount * target_q_values
q_target = q_target.detach()
q_pred = self.qfs[idx](obs, actions)
bellman_errors.append((q_pred - q_target)**2)
"""
Update Networks
"""
if self.stop_critic_training is None or self.stop_critic_training >= self.current_epoch:
for idx in range(self.num_q):
self.qf_optimizers[idx].zero_grad()
bellman_errors[idx].mean().backward()
self.qf_optimizers[idx].step()
policy_actions = policy_loss = None
var_q_grad_sum = None
if self._n_train_steps_total % self.policy_and_target_update_period == 0:
"""
update target network
"""
if self.stop_actor_training is None or self.stop_actor_training >= self.current_epoch:
policy_actions = self.policy(obs)
policy_actions.retain_grad()
q_output = self.qfs[0](obs, policy_actions)
policy_loss = -q_output.mean()
self.policy_optimizer.zero_grad()
policy_loss.backward()
self.policy_optimizer.step()
ptu.soft_update_from_to(self.policy, self.target_policy,
self.tau)
for idx in range(self.num_q):
ptu.soft_update_from_to(self.qfs[idx],
self.target_qfs[idx], self.tau)
if self.eval_statistics is None:
"""
Eval should set this to None.
This way, these statistics are only computed for one batch.
"""
if policy_loss is None:
"""
Compute the policy loss which computed by Q-value functions
"""
policy_actions = self.policy(obs)
q_output = self.qfs[0](obs, policy_actions)
policy_loss = -q_output.mean()
if var_q_grad_sum is None:
"""
Compute the gradient of taking different variables
"""
ensemble_q_grads = []
ensemble_j_grads = []
for idx in range(self.num_q):
policy_actions = self.policy(obs)
policy_actions.retain_grad()
q_output = self.qfs[idx](obs, policy_actions)
policy_loss = -q_output.mean()
self.policy_optimizer.zero_grad()
policy_loss.backward()
ensemble_q_grads.append(
torch.mean(policy_actions.grad, 0).view(1, -1))
all_grad = torch.cat(
[(torch.squeeze(param.grad.view(1, -1))).view(1, -1)
for param in self.policy.parameters()],
dim=1)
ensemble_j_grads.append(all_grad)
ensemble_q_grads = torch.cat(ensemble_q_grads)
ensemble_j_grads = torch.cat(ensemble_j_grads)
average_g_grads = torch.mean(ensemble_q_grads, dim=0)
average_j_grads = torch.mean(ensemble_j_grads, dim=0)
average_g_grad_norm = torch.norm(average_g_grads, p=2)
average_j_grad_norm = torch.norm(average_j_grads, p=2)
var_q_grads = ensemble_q_grads.std(dim=0)**2
var_j_grads = ensemble_j_grads.std(dim=0)**2
var_q_grad_sum = torch.sum(var_q_grads)
var_j_grad_sum = torch.sum(var_j_grads)
self.eval_statistics = OrderedDict()
self.eval_statistics['Policy Loss'] = np.mean(
ptu.get_numpy(policy_loss))
self.eval_statistics['Analysis: Var Q gradients'] = np.mean(
ptu.get_numpy(var_q_grad_sum))
self.eval_statistics['Analysis: Var J gradients'] = np.mean(
ptu.get_numpy(var_j_grad_sum))
self.eval_statistics['Analysis: Mean Q grad norm'] = np.mean(
ptu.get_numpy(average_g_grad_norm))
self.eval_statistics['Analysis: Mean J grad norm'] = np.mean(
ptu.get_numpy(average_j_grad_norm))
def pretrain_policy_with_bc(
self,
policy,
train_buffer,
test_buffer,
steps,
label="policy",
):
logger.remove_tabular_output(
'progress.csv',
relative_to_snapshot_dir=True,
)
logger.add_tabular_output(
'pretrain_%s.csv' % label,
relative_to_snapshot_dir=True,
)
optimizer = self.optimizers[policy]
prev_time = time.time()
for i in range(steps):
train_policy_loss, train_logp_loss, train_mse_loss, train_stats = self.run_bc_batch(
train_buffer, policy)
train_policy_loss = train_policy_loss * self.bc_weight
optimizer.zero_grad()
train_policy_loss.backward()
optimizer.step()
test_policy_loss, test_logp_loss, test_mse_loss, test_stats = self.run_bc_batch(
test_buffer, policy)
test_policy_loss = test_policy_loss * self.bc_weight
if i % self.pretraining_logging_period == 0:
stats = {
"pretrain_bc/batch":
i,
"pretrain_bc/Train Logprob Loss":
ptu.get_numpy(train_logp_loss),
"pretrain_bc/Test Logprob Loss":
ptu.get_numpy(test_logp_loss),
"pretrain_bc/Train MSE":
ptu.get_numpy(train_mse_loss),
"pretrain_bc/Test MSE":
ptu.get_numpy(test_mse_loss),
"pretrain_bc/train_policy_loss":
ptu.get_numpy(train_policy_loss),
"pretrain_bc/test_policy_loss":
ptu.get_numpy(test_policy_loss),
"pretrain_bc/epoch_time":
time.time() - prev_time,
}
logger.record_dict(stats)
logger.dump_tabular(with_prefix=True, with_timestamp=False)
pickle.dump(
self.policy,
open(logger.get_snapshot_dir() + '/bc_%s.pkl' % label,
"wb"))
prev_time = time.time()
logger.remove_tabular_output(
'pretrain_%s.csv' % label,
relative_to_snapshot_dir=True,
)
logger.add_tabular_output(
'progress.csv',
relative_to_snapshot_dir=True,
)
if self.post_bc_pretrain_hyperparams:
self.set_algorithm_weights(**self.post_bc_pretrain_hyperparams)
def train_from_torch(self, batch):
self._current_epoch += 1
rewards = batch['rewards']
terminals = batch['terminals']
obs = batch['observations']
actions = batch['actions']
next_obs = batch['next_observations']
"""
Behavior clone a policy
"""
recon, mean, std = self.vae(obs, actions)
recon_loss = self.qf_criterion(recon, actions)
kl_loss = -0.5 * (1 + torch.log(std.pow(2)) - mean.pow(2) -
std.pow(2)).mean()
vae_loss = recon_loss + 0.5 * kl_loss
self.vae_optimizer.zero_grad()
vae_loss.backward()
self.vae_optimizer.step()
"""
Critic Training
"""
with torch.no_grad():
# Duplicate state 10 times (10 is a hyperparameter chosen by BCQ)
state_rep = next_obs.unsqueeze(1).repeat(1, 10, 1).view(
next_obs.shape[0] * 10, next_obs.shape[1]) # 10BxS
# Compute value of perturbed actions sampled from the VAE
action_rep = self.policy(state_rep)[0] # 10BxA
target_qf1 = self.target_qf1(state_rep, action_rep) # 10Bx1
target_qf2 = self.target_qf2(state_rep, action_rep) # 10Bx1
# Soft Clipped Double Q-learning
target_Q = 0.75 * torch.min(target_qf1,
target_qf2) + 0.25 * torch.max(
target_qf1, target_qf2) # 10Bx1
max_target_action = target_Q.view(next_obs.shape[0],
-1).max(1) # 10Bx1 > Bx10 > B,B
target_Q = max_target_action[0].view(-1, 1) # B > Bx1
# 10BxA > Bx10xA > BxA
max_actions = action_rep.view(
next_obs.shape[0], 10,
action_rep.shape[1])[torch.arange(next_obs.shape[0]),
max_target_action[1]]
target_Q = self.reward_scale * rewards + (
1.0 - terminals) * self.discount * target_Q # Bx1
qf1_pred = self.qf1(obs, actions) # Bx1
qf2_pred = self.qf2(obs, actions) # Bx1
critic_unc = uncertainty(next_obs, max_actions, self.T, self.beta,
self.pre_model, self.pre_model_name)
qf1_loss = ((qf1_pred - target_Q.detach()).pow(2) * critic_unc).mean()
qf2_loss = ((qf2_pred - target_Q.detach()).pow(2) * critic_unc).mean()
"""
Actor Training
"""
sampled_actions, raw_sampled_actions = self.vae.decode_multiple(
obs, num_decode=self.num_samples_mmd_match)
actor_samples, _, _, _, _, _, _, raw_actor_actions = self.policy(
obs.unsqueeze(1).repeat(1, self.num_samples_mmd_match,
1).view(-1, obs.shape[1]),
return_log_prob=True)
actor_samples = actor_samples.view(obs.shape[0],
self.num_samples_mmd_match,
actions.shape[1])
raw_actor_actions = raw_actor_actions.view(obs.shape[0],
self.num_samples_mmd_match,
actions.shape[1])
if self.kernel_choice == 'laplacian':
mmd_loss = self.mmd_loss_laplacian(raw_sampled_actions,
raw_actor_actions,
sigma=self.mmd_sigma)
elif self.kernel_choice == 'gaussian':
mmd_loss = self.mmd_loss_gaussian(raw_sampled_actions,
raw_actor_actions,
sigma=self.mmd_sigma)
action_divergence = ((sampled_actions - actor_samples)**2).sum(-1)
raw_action_divergence = ((raw_sampled_actions -
raw_actor_actions)**2).sum(-1)
q_val1 = self.qf1(obs, actor_samples[:, 0, :])
q_val2 = self.qf2(obs, actor_samples[:, 0, :])
actor_unc = uncertainty(obs, actor_samples[:, 0, :], self.T, self.beta,
self.pre_model, self.pre_model_name)
if self.policy_update_style == '0':
policy_loss = torch.min(q_val1, q_val2)[:, 0] * actor_unc[:, 0]
elif self.policy_update_style == '1':
policy_loss = torch.mean(q_val1, q_val2)[:, 0] * actor_unc[:, 0]
# Use uncertainty after some epochs
if self._n_train_steps_total >= 40000:
if self.mode == 'auto':
policy_loss = (-policy_loss + self.log_alpha.exp() *
(mmd_loss - self.target_mmd_thresh)).mean()
else:
policy_loss = (-policy_loss + 100 * mmd_loss).mean()
else:
if self.mode == 'auto':
policy_loss = (self.log_alpha.exp() *
(mmd_loss - self.target_mmd_thresh)).mean()
else:
policy_loss = 100 * mmd_loss.mean()
"""
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()
if self.mode == 'auto':
policy_loss.backward(retain_graph=True)
self.policy_optimizer.step()
if self.mode == 'auto':
self.alpha_optimizer.zero_grad()
(-policy_loss).backward()
self.alpha_optimizer.step()
self.log_alpha.data.clamp_(min=-5.0, max=10.0)
"""
Update networks
"""
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)
"""
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.
"""
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['Num Q Updates'] = self._num_q_update_steps
self.eval_statistics[
'Num Policy Updates'] = self._num_policy_update_steps
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(qf1_pred),
))
self.eval_statistics.update(
create_stats_ordered_dict(
'Q2 Predictions',
ptu.get_numpy(qf2_pred),
))
self.eval_statistics.update(
create_stats_ordered_dict(
'Q Targets',
ptu.get_numpy(target_Q),
))
self.eval_statistics.update(
create_stats_ordered_dict('MMD Loss', ptu.get_numpy(mmd_loss)))
self.eval_statistics.update(
create_stats_ordered_dict('Action Divergence',
ptu.get_numpy(action_divergence)))
self.eval_statistics.update(
create_stats_ordered_dict(
'Raw Action Divergence',
ptu.get_numpy(raw_action_divergence)))
if self.mode == 'auto':
self.eval_statistics['Alpha'] = self.log_alpha.exp().item()
self._n_train_steps_total += 1
def encode(self, obs):
if self.normalize:
return ptu.get_numpy(self.model.encode(
ptu.from_numpy(obs) / 255.0))
return ptu.get_numpy(self.model.encode(ptu.from_numpy(obs)))
def train_from_torch(self, batch):
rewards = batch['rewards']
terminals = batch['terminals']
obs = batch['observations']
actions = batch['actions']
next_obs = batch['next_observations']
"""
Classifier and Policy
"""
class_actions = self.policy(obs)
class_prob = self.classifier(obs, actions)
prob_target = 1 + rewards[:, -1] #MAYBE INSTEAD PREDICT WHOLE ARRAY? THEN JUST USE LAST ONE
neg_log_prob = - torch.log(self.classifier(obs, class_actions))
policy_loss = (neg_log_prob).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
def train_from_torch(self, batch):
rewards = batch['rewards']
terminals = batch['terminals']
obs = batch['observations']
actions = batch['actions']
next_obs = batch['next_observations']
"""
Critic operations.
"""
next_actions = self.target_policy(next_obs)
noise = ptu.randn(next_actions.shape) * self.target_policy_noise
noise = torch.clamp(noise, -self.target_policy_noise_clip,
self.target_policy_noise_clip)
noisy_next_actions = next_actions + noise
target_q1_values = self.target_qf1(next_obs, noisy_next_actions)
target_q2_values = self.target_qf2(next_obs, noisy_next_actions)
target_q_values = torch.min(target_q1_values, target_q2_values)
q_target = self.reward_scale * rewards + (
1. - terminals) * self.discount * target_q_values
q_target = q_target.detach()
q1_pred = self.qf1(obs, actions)
bellman_errors_1 = (q1_pred - q_target)**2
qf1_loss = bellman_errors_1.mean()
q2_pred = self.qf2(obs, actions)
bellman_errors_2 = (q2_pred - q_target)**2
qf2_loss = bellman_errors_2.mean()
"""
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()
policy_actions = policy_loss = None
if self._n_train_steps_total % self.policy_and_target_update_period == 0:
policy_actions = self.policy(obs)
q_output = self.qf1(obs, policy_actions)
policy_loss = -q_output.mean()
self.policy_optimizer.zero_grad()
policy_loss.backward()
self.policy_optimizer.step()
ptu.soft_update_from_to(self.policy, self.target_policy, self.tau)
ptu.soft_update_from_to(self.qf1, self.target_qf1, self.tau)
ptu.soft_update_from_to(self.qf2, self.target_qf2, self.tau)
if self._need_to_update_eval_statistics:
self._need_to_update_eval_statistics = False
if policy_loss is None:
policy_actions = self.policy(obs)
q_output = self.qf1(obs, policy_actions)
policy_loss = -q_output.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(
'Bellman Errors 1',
ptu.get_numpy(bellman_errors_1),
))
self.eval_statistics.update(
create_stats_ordered_dict(
'Bellman Errors 2',
ptu.get_numpy(bellman_errors_2),
))
for i in range(policy_actions.shape[1]):
self.eval_statistics.update(
create_stats_ordered_dict(
'Policy Action%s' % i,
ptu.get_numpy(policy_actions[:, i:i + 1]),
))
policy_grads = np.array([
np.linalg.norm(ptu.get_numpy(a.grad))
if a.grad is not None else [0]
for a in self.policy.parameters()
]).flatten()
pg_stats = OrderedDict({})
pg_stats["Policy Gradient Norm/Mean"] = np.mean(policy_grads)
self.eval_statistics.update(pg_stats)
self._n_train_steps_total += 1
def train_from_torch(self, batch, fast_batch):
rewards = batch['rewards']
terminals = batch['terminals']
obs = batch['observations']
actions = batch['actions']
next_obs = batch['next_observations']
"""
Prob Clasifier
"""
if self.imp:
fast_obs = fast_batch['observations']
fast_actions = fast_batch['actions']
slow_samples = torch.cat((obs, actions), dim=1)
fast_samples = torch.cat((fast_obs, fast_actions), dim=1)
zeros = torch.zeros(slow_samples.size(0))
ones = torch.ones(fast_samples.size(0))
if obs.is_cuda:
zeros = zeros.cuda()
ones = ones.cuda()
slow_preds = self.prob_classifier(slow_samples)
fast_preds = self.prob_classifier(fast_samples)
loss = F.binary_cross_entropy(F.sigmoid(slow_preds), zeros) + \
F.binary_cross_entropy(F.sigmoid(fast_preds), ones)
if self._n_train_steps_total % 1000 == 0:
print(loss)
self.prob_classifier_optimizer.zero_grad()
loss.backward()
self.prob_classifier_optimizer.step()
importance_weights = F.sigmoid(slow_preds/self.temperature).detach()
importance_weights = importance_weights / torch.sum(importance_weights)
"""
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),
)
if self.imp and self.policy_net:
policy_loss = (alpha*log_pi - q_new_actions)
policy_loss = (policy_loss * importance_weights.detach()).sum()
else:
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
if self.imp and self.q_net and self.residual:
q1_imp = (q1_pred - q_target.detach()) * importance_weights.detach()
q2_imp = (q2_pred - q_target.detach()) * importance_weights.detach()
# qf1_loss = self.qf_criterion(q1_pred, q_target.detach())
# qf2_loss = self.qf_criterion(q2_pred, q_target.detach())
qf1_loss = (q1_imp ** 2).sum()
qf2_loss = (q2_imp ** 2).sum()
# qf1_loss = (qf1_loss * importance_weights.detach()).sum()
# qf2_loss = (qf2_loss * importance_weights.detach()).sum()
elif self.imp and self.q_net and not self.residual:
qf1_loss = self.qf_criterion(q1_pred, q_target.detach())
qf2_loss = self.qf_criterion(q2_pred, q_target.detach())
qf1_loss = (qf1_loss * importance_weights.detach()).sum()
qf2_loss = (qf2_loss * importance_weights.detach()).sum()
else:
qf1_loss = self.qf_criterion(q1_pred, q_target.detach()).mean()
qf2_loss = self.qf_criterion(q2_pred, q_target.detach()).mean()
"""
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
def decode_np(self, inputs, cont=True):
return np.clip(
ptu.get_numpy(self.decode(ptu.from_numpy(inputs), cont=cont)), 0,
1)
def test_from_torch(self, batch):
rewards = batch['rewards']
terminals = batch['terminals']
obs = batch['observations']
actions = batch['actions']
next_obs = batch['next_observations']
weights = batch.get('weights', None)
if self.reward_transform:
rewards = self.reward_transform(rewards)
if self.terminal_transform:
terminals = self.terminal_transform(terminals)
"""
Policy and Alpha Loss
"""
dist = self.policy(obs)
new_obs_actions, log_pi = dist.rsample_and_logprob()
policy_mle = dist.mle_estimate()
if self.use_automatic_entropy_tuning:
alpha_loss = -(self.log_alpha *
(log_pi + self.target_entropy).detach()).mean()
alpha = self.log_alpha.exp()
else:
alpha_loss = 0
alpha = self.alpha
q1_pred = self.qf1(obs, actions)
q2_pred = self.qf2(obs, actions)
# Make sure policy accounts for squashing functions like tanh correctly!
next_dist = self.policy(next_obs)
new_next_actions, new_log_pi = next_dist.rsample_and_logprob()
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())
qf1_new_actions = self.qf1(obs, new_obs_actions)
qf2_new_actions = self.qf2(obs, new_obs_actions)
q_new_actions = torch.min(
qf1_new_actions,
qf2_new_actions,
)
policy_loss = (log_pi - q_new_actions).mean()
self.eval_statistics['validation/QF1 Loss'] = np.mean(
ptu.get_numpy(qf1_loss))
self.eval_statistics['validation/QF2 Loss'] = np.mean(
ptu.get_numpy(qf2_loss))
self.eval_statistics['validation/Policy Loss'] = np.mean(
ptu.get_numpy(policy_loss))
self.eval_statistics.update(
create_stats_ordered_dict(
'validation/Q1 Predictions',
ptu.get_numpy(q1_pred),
))
self.eval_statistics.update(
create_stats_ordered_dict(
'validation/Q2 Predictions',
ptu.get_numpy(q2_pred),
))
self.eval_statistics.update(
create_stats_ordered_dict(
'validation/Q Targets',
ptu.get_numpy(q_target),
))
self.eval_statistics.update(
create_stats_ordered_dict(
'validation/Log Pis',
ptu.get_numpy(log_pi),
))
policy_statistics = add_prefix(dist.get_diagnostics(),
"validation/policy/")
self.eval_statistics.update(policy_statistics)
def _take_step(self, indices, context):
num_tasks = len(indices)
# data is (task, batch, feat)
# obs, actions, rewards, next_obs, terms = self.sample_data(indices)#从Replay Buffer采集数据，s,a,r,s',d
obs, actions, rewards, next_obs, terms = self.sample_sac(indices)
# run inference in networks
# policy_outputs, task_z = self.agent(obs, context)#策略forward的输出，以及任务隐变量Z
policy_outputs, task_z = self.agent(obs, context) # 策略forward的输出，以及任务隐变量Z
new_actions, policy_mean, policy_log_std, log_pi = policy_outputs[:4] # 下一个状态下策略所采取的动作，其log概率 line 63
# flattens out the task dimension:
t, b, _ = obs.size()
obs = obs.view(t * b, -1)
actions = actions.view(t * b, -1)
next_obs = next_obs.view(t * b, -1)
rewards_flat = rewards.view(self.batch_size * num_tasks, -1)
# scale rewards for Bellman update
rewards_flat = rewards_flat * self.reward_scale
terms_flat = terms.view(self.batch_size * num_tasks, -1)
with torch.no_grad():
q1_next_target, q2_next_target = self.critic_target(next_obs, new_actions,
task_z) # target q line 64
min_qf_next_target = torch.min(q1_next_target, q2_next_target) # 计算较小的target Q line 65
next_q_value = rewards_flat + (1. - terms_flat) * self.discount * (
min_qf_next_target - self.alpha * log_pi) # q=r+(1-d)γ(Vst+1) line 66
q1, q2 = self.critic(obs, actions,
task_z) # forward line 68
q1_loss = F.mse_loss(q1,
next_q_value) # JQ = 𝔼(st,at)~D[0.5(Q1(st,at) - r(st,at) - γ(𝔼st+1~p[V(st+1)]))^2] line 69
q2_loss = F.mse_loss(q2, next_q_value) # line 70
# pi, log_pi, _ = self.agent.policy.sample(obs) # 动作，动作的对数概率
# print(obs.size()) #[1024,27]
# print(task_z.size())
in_policy = torch.cat([obs, task_z], 1)
pi, _, _, log_pi, _, _, _, _, = self.agent.policy(in_policy) # line 72
q1_pi, q2_pi = self.critic(obs, pi,
task_z.detach()) # 动作的Q值 line 74
min_q_pi = torch.min(q1_pi, q2_pi) # line 75
# KL constraint on z if probabilistic
self.context_optimizer.zero_grad()
if self.use_information_bottleneck:
kl_div = self.agent.compute_kl_div() # context_encoder前向传播得到μ和σ，计算该分布与先验分布的kl散度
kl_loss = self.kl_lambda * kl_div
kl_loss.backward(retain_graph=True)
self.critic_optimizer.zero_grad()
q1_loss.backward(retain_graph=True)
self.critic_optimizer.step()
self.critic_optimizer.zero_grad()
q2_loss.backward(retain_graph=True)
self.critic_optimizer.step()
self.context_optimizer.step()
soft_update(self.critic_target, self.critic, self.soft_target_tau)
policy_loss = ((self.alpha * log_pi) - min_q_pi).mean() # line 77
self.policy_optimizer.zero_grad()
policy_loss.backward(retain_graph=True)
self.policy_optimizer.step()
if self.automatic_entropy_tuning:
alpha_loss = -(self.log_alpha * (log_pi + self.target_entropy).detach()).mean() # E[-αlogπ(at|st)-αH]
self.alpha_optim.zero_grad()
alpha_loss.backward(retain_graph=True)
self.alpha_optim.step()
self.alpha = self.log_alpha.exp()
else:
alpha_loss = 0
alpha = 1
# save some statistics for eval
if self.eval_statistics is None:
# eval should set this to None.
# this way, these statistics are only computed for one batch.
self.eval_statistics = OrderedDict()
if self.use_information_bottleneck:
z_mean = np.mean(np.abs(ptu.get_numpy(self.agent.z_means[0])))
z_sig = np.mean(ptu.get_numpy(self.agent.z_vars[0]))
self.eval_statistics['Z mean train'] = z_mean
self.eval_statistics['Z variance train'] = z_sig
self.eval_statistics['KL Divergence'] = ptu.get_numpy(kl_div)
self.eval_statistics['KL Loss'] = ptu.get_numpy(kl_loss)
self.eval_statistics['Q1 Loss'] = np.mean(ptu.get_numpy(q1_loss))
self.eval_statistics['Q2 Loss'] = np.mean(ptu.get_numpy(q2_loss))
self.eval_statistics['Policy Loss'] = np.mean(ptu.get_numpy(
policy_loss
))
if self.automatic_entropy_tuning:
self.eval_statistics['Alpha Loss'] = np.mean(ptu.get_numpy(alpha_loss))
self.eval_statistics['Alpha'] = np.mean(ptu.get_numpy(self.alpha))
self.eval_statistics.update(create_stats_ordered_dict(
'Q1 Predictions',
ptu.get_numpy(q1),
))
self.eval_statistics.update(create_stats_ordered_dict(
'Q2 Predictions',
ptu.get_numpy(q2),
))
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),
))
def train_from_torch(
self,
batch,
train=True,
pretrain=False,
):
rewards = batch['rewards']
terminals = batch['terminals']
obs = batch['observations']
actions = batch['actions']
next_obs = batch['next_observations']
weights = batch.get('weights', None)
if self.reward_transform:
rewards = self.reward_transform(rewards)
if self.terminal_transform:
terminals = self.terminal_transform(terminals)
"""
Policy and Alpha Loss
"""
dist = self.policy(obs)
new_obs_actions, log_pi = dist.rsample_and_logprob()
policy_mle = dist.mle_estimate()
if self.brac:
buf_dist = self.buffer_policy(obs)
buf_log_pi = buf_dist.log_prob(actions)
rewards = rewards + buf_log_pi
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 = self.alpha
"""
QF Loss
"""
q1_pred = self.qf1(obs, actions)
q2_pred = self.qf2(obs, actions)
# Make sure policy accounts for squashing functions like tanh correctly!
next_dist = self.policy(next_obs)
new_next_actions, new_log_pi = next_dist.rsample_and_logprob()
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())
"""
Policy Loss
"""
qf1_new_actions = self.qf1(obs, new_obs_actions)
qf2_new_actions = self.qf2(obs, new_obs_actions)
q_new_actions = torch.min(
qf1_new_actions,
qf2_new_actions,
)
# Advantage-weighted regression
if self.awr_use_mle_for_vf:
v1_pi = self.qf1(obs, policy_mle)
v2_pi = self.qf2(obs, policy_mle)
v_pi = torch.min(v1_pi, v2_pi)
else:
if self.vf_K > 1:
vs = []
for i in range(self.vf_K):
u = dist.sample()
q1 = self.qf1(obs, u)
q2 = self.qf2(obs, u)
v = torch.min(q1, q2)
# v = q1
vs.append(v)
v_pi = torch.cat(vs, 1).mean(dim=1)
else:
# v_pi = self.qf1(obs, new_obs_actions)
v1_pi = self.qf1(obs, new_obs_actions)
v2_pi = self.qf2(obs, new_obs_actions)
v_pi = torch.min(v1_pi, v2_pi)
if self.awr_sample_actions:
u = new_obs_actions
if self.awr_min_q:
q_adv = q_new_actions
else:
q_adv = qf1_new_actions
elif self.buffer_policy_sample_actions:
buf_dist = self.buffer_policy(obs)
u, _ = buf_dist.rsample_and_logprob()
qf1_buffer_actions = self.qf1(obs, u)
qf2_buffer_actions = self.qf2(obs, u)
q_buffer_actions = torch.min(
qf1_buffer_actions,
qf2_buffer_actions,
)
if self.awr_min_q:
q_adv = q_buffer_actions
else:
q_adv = qf1_buffer_actions
else:
u = actions
if self.awr_min_q:
q_adv = torch.min(q1_pred, q2_pred)
else:
q_adv = q1_pred
policy_logpp = dist.log_prob(u)
if self.use_automatic_beta_tuning:
buffer_dist = self.buffer_policy(obs)
beta = self.log_beta.exp()
kldiv = torch.distributions.kl.kl_divergence(dist, buffer_dist)
beta_loss = -1 * (beta *
(kldiv - self.beta_epsilon).detach()).mean()
self.beta_optimizer.zero_grad()
beta_loss.backward()
self.beta_optimizer.step()
else:
beta = self.beta_schedule.get_value(self._n_train_steps_total)
if self.normalize_over_state == "advantage":
score = q_adv - v_pi
if self.mask_positive_advantage:
score = torch.sign(score)
elif self.normalize_over_state == "Z":
buffer_dist = self.buffer_policy(obs)
K = self.Z_K
buffer_obs = []
buffer_actions = []
log_bs = []
log_pis = []
for i in range(K):
u = buffer_dist.sample()
log_b = buffer_dist.log_prob(u)
log_pi = dist.log_prob(u)
buffer_obs.append(obs)
buffer_actions.append(u)
log_bs.append(log_b)
log_pis.append(log_pi)
buffer_obs = torch.cat(buffer_obs, 0)
buffer_actions = torch.cat(buffer_actions, 0)
p_buffer = torch.exp(torch.cat(log_bs, 0).sum(dim=1, ))
log_pi = torch.cat(log_pis, 0)
log_pi = log_pi.sum(dim=1, )
q1_b = self.qf1(buffer_obs, buffer_actions)
q2_b = self.qf2(buffer_obs, buffer_actions)
q_b = torch.min(q1_b, q2_b)
q_b = torch.reshape(q_b, (-1, K))
adv_b = q_b - v_pi
# if self._n_train_steps_total % 100 == 0:
# import ipdb; ipdb.set_trace()
# Z = torch.exp(adv_b / beta).mean(dim=1, keepdim=True)
# score = torch.exp((q_adv - v_pi) / beta) / Z
# score = score / sum(score)
logK = torch.log(ptu.tensor(float(K)))
logZ = torch.logsumexp(adv_b / beta - logK, dim=1, keepdim=True)
logS = (q_adv - v_pi) / beta - logZ
# logZ = torch.logsumexp(q_b/beta - logK, dim=1, keepdim=True)
# logS = q_adv/beta - logZ
score = F.softmax(logS, dim=0) # score / sum(score)
else:
error
if self.clip_score is not None:
score = torch.clamp(score, max=self.clip_score)
if self.weight_loss and weights is None:
if self.normalize_over_batch == True:
weights = F.softmax(score / beta, dim=0)
elif self.normalize_over_batch == "whiten":
adv_mean = torch.mean(score)
adv_std = torch.std(score) + 1e-5
normalized_score = (score - adv_mean) / adv_std
weights = torch.exp(normalized_score / beta)
elif self.normalize_over_batch == "exp":
weights = torch.exp(score / beta)
elif self.normalize_over_batch == "step_fn":
weights = (score > 0).float()
elif self.normalize_over_batch == False:
weights = score
else:
error
weights = weights[:, 0]
policy_loss = alpha * log_pi.mean()
if self.use_awr_update and self.weight_loss:
policy_loss = policy_loss + self.awr_weight * (
-policy_logpp * len(weights) * weights.detach()).mean()
elif self.use_awr_update:
policy_loss = policy_loss + self.awr_weight * (
-policy_logpp).mean()
if self.use_reparam_update:
policy_loss = policy_loss + self.reparam_weight * (
-q_new_actions).mean()
policy_loss = self.rl_weight * policy_loss
if self.compute_bc:
train_policy_loss, train_logp_loss, train_mse_loss, _ = self.run_bc_batch(
self.demo_train_buffer, self.policy)
policy_loss = policy_loss + self.bc_weight * train_policy_loss
if not pretrain and self.buffer_policy_reset_period > 0 and self._n_train_steps_total % self.buffer_policy_reset_period == 0:
del self.buffer_policy_optimizer
self.buffer_policy_optimizer = self.optimizer_class(
self.buffer_policy.parameters(),
weight_decay=self.policy_weight_decay,
lr=self.policy_lr,
)
self.optimizers[self.buffer_policy] = self.buffer_policy_optimizer
for i in range(self.num_buffer_policy_train_steps_on_reset):
if self.train_bc_on_rl_buffer:
if self.advantage_weighted_buffer_loss:
buffer_dist = self.buffer_policy(obs)
buffer_u = actions
buffer_new_obs_actions, _ = buffer_dist.rsample_and_logprob(
)
buffer_policy_logpp = buffer_dist.log_prob(buffer_u)
buffer_policy_logpp = buffer_policy_logpp[:, None]
buffer_q1_pred = self.qf1(obs, buffer_u)
buffer_q2_pred = self.qf2(obs, buffer_u)
buffer_q_adv = torch.min(buffer_q1_pred,
buffer_q2_pred)
buffer_v1_pi = self.qf1(obs, buffer_new_obs_actions)
buffer_v2_pi = self.qf2(obs, buffer_new_obs_actions)
buffer_v_pi = torch.min(buffer_v1_pi, buffer_v2_pi)
buffer_score = buffer_q_adv - buffer_v_pi
buffer_weights = F.softmax(buffer_score / beta, dim=0)
buffer_policy_loss = self.awr_weight * (
-buffer_policy_logpp * len(buffer_weights) *
buffer_weights.detach()).mean()
else:
buffer_policy_loss, buffer_train_logp_loss, buffer_train_mse_loss, _ = self.run_bc_batch(
self.replay_buffer.train_replay_buffer,
self.buffer_policy)
self.buffer_policy_optimizer.zero_grad()
buffer_policy_loss.backward(retain_graph=True)
self.buffer_policy_optimizer.step()
if self.train_bc_on_rl_buffer:
if self.advantage_weighted_buffer_loss:
buffer_dist = self.buffer_policy(obs)
buffer_u = actions
buffer_new_obs_actions, _ = buffer_dist.rsample_and_logprob()
buffer_policy_logpp = buffer_dist.log_prob(buffer_u)
buffer_policy_logpp = buffer_policy_logpp[:, None]
buffer_q1_pred = self.qf1(obs, buffer_u)
buffer_q2_pred = self.qf2(obs, buffer_u)
buffer_q_adv = torch.min(buffer_q1_pred, buffer_q2_pred)
buffer_v1_pi = self.qf1(obs, buffer_new_obs_actions)
buffer_v2_pi = self.qf2(obs, buffer_new_obs_actions)
buffer_v_pi = torch.min(buffer_v1_pi, buffer_v2_pi)
buffer_score = buffer_q_adv - buffer_v_pi
buffer_weights = F.softmax(buffer_score / beta, dim=0)
buffer_policy_loss = self.awr_weight * (
-buffer_policy_logpp * len(buffer_weights) *
buffer_weights.detach()).mean()
else:
buffer_policy_loss, buffer_train_logp_loss, buffer_train_mse_loss, _ = self.run_bc_batch(
self.replay_buffer.train_replay_buffer, self.buffer_policy)
"""
Update networks
"""
if self._n_train_steps_total % self.q_update_period == 0:
self.qf1_optimizer.zero_grad()
qf1_loss.backward()
self.qf1_optimizer.step()
self.qf2_optimizer.zero_grad()
qf2_loss.backward()
self.qf2_optimizer.step()
if self._n_train_steps_total % self.policy_update_period == 0 and self.update_policy:
self.policy_optimizer.zero_grad()
policy_loss.backward()
self.policy_optimizer.step()
if self.train_bc_on_rl_buffer and self._n_train_steps_total % self.policy_update_period == 0:
self.buffer_policy_optimizer.zero_grad()
buffer_policy_loss.backward()
self.buffer_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(
'rewards',
ptu.get_numpy(rewards),
))
self.eval_statistics.update(
create_stats_ordered_dict(
'terminals',
ptu.get_numpy(terminals),
))
policy_statistics = add_prefix(dist.get_diagnostics(), "policy/")
self.eval_statistics.update(policy_statistics)
self.eval_statistics.update(
create_stats_ordered_dict(
'Advantage Weights',
ptu.get_numpy(weights),
))
self.eval_statistics.update(
create_stats_ordered_dict(
'Advantage Score',
ptu.get_numpy(score),
))
if self.normalize_over_state == "Z":
self.eval_statistics.update(
create_stats_ordered_dict(
'logZ',
ptu.get_numpy(logZ),
))
if self.use_automatic_entropy_tuning:
self.eval_statistics['Alpha'] = alpha.item()
self.eval_statistics['Alpha Loss'] = alpha_loss.item()
if self.compute_bc:
test_policy_loss, test_logp_loss, test_mse_loss, _ = self.run_bc_batch(
self.demo_test_buffer, self.policy)
self.eval_statistics.update({
"bc/Train Logprob Loss":
ptu.get_numpy(train_logp_loss),
"bc/Test Logprob Loss":
ptu.get_numpy(test_logp_loss),
"bc/Train MSE":
ptu.get_numpy(train_mse_loss),
"bc/Test MSE":
ptu.get_numpy(test_mse_loss),
"bc/train_policy_loss":
ptu.get_numpy(train_policy_loss),
"bc/test_policy_loss":
ptu.get_numpy(test_policy_loss),
})
if self.train_bc_on_rl_buffer:
_, buffer_train_logp_loss, _, _ = self.run_bc_batch(
self.replay_buffer.train_replay_buffer, self.buffer_policy)
_, buffer_test_logp_loss, _, _ = self.run_bc_batch(
self.replay_buffer.validation_replay_buffer,
self.buffer_policy)
buffer_dist = self.buffer_policy(obs)
kldiv = torch.distributions.kl.kl_divergence(dist, buffer_dist)
_, train_offline_logp_loss, _, _ = self.run_bc_batch(
self.demo_train_buffer, self.buffer_policy)
_, test_offline_logp_loss, _, _ = self.run_bc_batch(
self.demo_test_buffer, self.buffer_policy)
self.eval_statistics.update({
"buffer_policy/Train Online Logprob":
-1 * ptu.get_numpy(buffer_train_logp_loss),
"buffer_policy/Test Online Logprob":
-1 * ptu.get_numpy(buffer_test_logp_loss),
"buffer_policy/Train Offline Logprob":
-1 * ptu.get_numpy(train_offline_logp_loss),
"buffer_policy/Test Offline Logprob":
-1 * ptu.get_numpy(test_offline_logp_loss),
"buffer_policy/train_policy_loss":
ptu.get_numpy(buffer_policy_loss),
# "buffer_policy/test_policy_loss": ptu.get_numpy(buffer_test_policy_loss),
"buffer_policy/kl_div":
ptu.get_numpy(kldiv.mean()),
})
if self.use_automatic_beta_tuning:
self.eval_statistics.update({
"adaptive_beta/beta":
ptu.get_numpy(beta.mean()),
"adaptive_beta/beta loss":
ptu.get_numpy(beta_loss.mean()),
})
if self.validation_qlearning:
train_data = self.replay_buffer.validation_replay_buffer.random_batch(
self.bc_batch_size)
train_data = np_to_pytorch_batch(train_data)
obs = train_data['observations']
next_obs = train_data['next_observations']
# goals = train_data['resampled_goals']
train_data[
'observations'] = obs # torch.cat((obs, goals), dim=1)
train_data[
'next_observations'] = next_obs # torch.cat((next_obs, goals), dim=1)
self.test_from_torch(train_data)
self._n_train_steps_total += 1
def np_ify(tensor_or_other):
if isinstance(tensor_or_other, 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']
goals = batch['goals']
num_steps_left = batch['num_steps_left']
"""
Policy operations.
"""
policy_actions, pre_tanh_value = self.policy(
obs,
goals,
num_steps_left,
return_preactivations=True,
)
pre_activation_policy_loss = ((pre_tanh_value**2).sum(dim=1).mean())
q_output = self.qf(
observations=obs,
actions=policy_actions,
num_steps_left=num_steps_left,
goals=goals,
)
raw_policy_loss = -q_output.mean()
policy_loss = (
raw_policy_loss +
pre_activation_policy_loss * self.policy_pre_activation_weight)
"""
Critic operations.
"""
next_actions = self.target_policy(
observations=next_obs,
goals=goals,
num_steps_left=num_steps_left - 1,
)
# speed up computation by not backpropping these gradients
next_actions.detach()
target_q_values = self.target_qf(
observations=next_obs,
actions=next_actions,
goals=goals,
num_steps_left=num_steps_left - 1,
)
q_target = rewards + (1. - terminals) * self.discount * target_q_values
q_target = q_target.detach()
q_pred = self.qf(
observations=obs,
actions=actions,
goals=goals,
num_steps_left=num_steps_left,
)
if self.tdm_normalizer:
q_pred = self.tdm_normalizer.distance_normalizer.normalize_scale(
q_pred)
q_target = self.tdm_normalizer.distance_normalizer.normalize_scale(
q_target)
bellman_errors = (q_pred - q_target)**2
qf_loss = self.qf_criterion(q_pred, q_target)
"""
Update Networks
"""
self.policy_optimizer.zero_grad()
policy_loss.backward()
self.policy_optimizer.step()
self.qf_optimizer.zero_grad()
qf_loss.backward()
self.qf_optimizer.step()
self._update_target_networks()
"""
Save some statistics for eval using just one batch.
"""
if self.need_to_update_eval_statistics:
self.need_to_update_eval_statistics = False
self.eval_statistics['QF Loss'] = np.mean(ptu.get_numpy(qf_loss))
self.eval_statistics['Policy Loss'] = np.mean(
ptu.get_numpy(policy_loss))
self.eval_statistics['Raw Policy Loss'] = np.mean(
ptu.get_numpy(raw_policy_loss))
self.eval_statistics['Preactivation Policy Loss'] = (
self.eval_statistics['Policy Loss'] -
self.eval_statistics['Raw Policy Loss'])
self.eval_statistics.update(
create_stats_ordered_dict(
'Q Predictions',
ptu.get_numpy(q_pred),
))
self.eval_statistics.update(
create_stats_ordered_dict(
'Q Targets',
ptu.get_numpy(q_target),
))
self.eval_statistics.update(
create_stats_ordered_dict(
'Bellman Errors',
ptu.get_numpy(bellman_errors),
))
self.eval_statistics.update(
create_stats_ordered_dict(
'Policy Action',
ptu.get_numpy(policy_actions),
))
def compute_loss(
self,
batch,
skip_statistics=False,
) -> Tuple[SACLosses, LossStatistics]:
rewards = batch['rewards']
terminals = batch['terminals']
obs = batch['observations']
actions = batch['actions']
next_obs = batch['next_observations']
"""
Policy and Alpha Loss
"""
dist = self.policy(obs)
new_obs_actions, log_pi = dist.rsample_and_logprob()
log_pi = log_pi.unsqueeze(-1)
if self.use_automatic_entropy_tuning:
alpha_loss = -(self.log_alpha *
(log_pi + self.target_entropy).detach()).mean()
alpha = self.log_alpha.exp()
else:
alpha_loss = 0
# alpha = 0
#Temperature Decay
alpha = self.log_alpha
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)
next_dist = self.policy(next_obs)
new_next_actions, new_log_pi = next_dist.rsample_and_logprob()
new_log_pi = new_log_pi.unsqueeze(-1)
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())
"""
Save some statistics for eval
"""
eval_statistics = OrderedDict()
if not skip_statistics:
eval_statistics['QF1 Loss'] = np.mean(ptu.get_numpy(qf1_loss))
eval_statistics['QF2 Loss'] = np.mean(ptu.get_numpy(qf2_loss))
eval_statistics['Policy Loss'] = np.mean(
ptu.get_numpy(policy_loss))
eval_statistics.update(
create_stats_ordered_dict(
'Q1 Predictions',
ptu.get_numpy(q1_pred),
))
eval_statistics.update(
create_stats_ordered_dict(
'Q2 Predictions',
ptu.get_numpy(q2_pred),
))
eval_statistics.update(
create_stats_ordered_dict(
'Q Targets',
ptu.get_numpy(q_target),
))
eval_statistics.update(
create_stats_ordered_dict(
'Log Pis',
ptu.get_numpy(log_pi),
))
policy_statistics = add_prefix(dist.get_diagnostics(), "policy/")
eval_statistics.update(policy_statistics)
if self.use_automatic_entropy_tuning:
eval_statistics['Alpha'] = alpha.item()
eval_statistics['Alpha Loss'] = alpha_loss.item()
else:
eval_statistics['Alpha'] = alpha
loss = SACLosses(
policy_loss=policy_loss,
qf1_loss=qf1_loss,
qf2_loss=qf2_loss,
alpha_loss=alpha_loss,
)
return loss, eval_statistics
def train_from_torch(self, batch):
rewards = batch['rewards']
terminals = batch['terminals']
obs = batch['observations']
actions = batch['actions']
raw_actions = batch['raw_actions']
next_obs = batch['next_observations']
"""
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 = torch.tensor(1.)
"""
Policy and VF Loss
"""
v1_pred = self.vf1(obs)
v2_pred = self.vf2(obs)
pi_pred = self.policy.log_prob(obs, actions, raw_actions)
target_v_values = torch.min(
self.target_vf1(next_obs),
self.target_vf2(next_obs),
)
# target_v_values = self.target_vf1(next_obs)
q_target = self.reward_scale * rewards + (
1. - terminals) * self.discount * target_v_values
vf1_loss = self.vf_criterion(
v1_pred,
q_target.detach() - (alpha * pi_pred).detach())
vf2_loss = self.vf_criterion(
v2_pred,
q_target.detach() - (alpha * pi_pred).detach())
policy_loss = self.pi_criterion(
alpha.detach() * pi_pred,
q_target.detach() - torch.min(v1_pred, v2_pred).detach())
# policy_loss = self.pi_criterion(alpha.detach()*pi_pred, q_target.detach()-v1_pred.detach())
"""
Update networks
"""
self.vf1_optimizer.zero_grad()
vf1_loss.backward()
if self.clip_gradient:
nn.utils.clip_grad_norm_(self.vf1.parameters(), self.clip_gradient)
self.vf1_optimizer.step()
self.vf2_optimizer.zero_grad()
vf2_loss.backward()
if self.clip_gradient:
nn.utils.clip_grad_norm_(self.vf2.parameters(), self.clip_gradient)
self.vf2_optimizer.step()
self.policy_optimizer.zero_grad()
policy_loss.backward()
if self.clip_gradient:
nn.utils.clip_grad_norm_(self.policy.parameters(),
self.clip_gradient)
self.policy_optimizer.step()
"""
Soft Updates
"""
if self._n_train_steps_total % self.target_update_period == 0:
ptu.soft_update_from_to(self.vf1, self.target_vf1,
self.soft_target_tau)
ptu.soft_update_from_to(self.vf2, self.target_vf2,
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.
"""
self.eval_statistics['VF1 Loss'] = np.mean(ptu.get_numpy(vf1_loss))
self.eval_statistics['VF2 Loss'] = np.mean(ptu.get_numpy(vf2_loss))
self.eval_statistics['Policy Loss'] = np.mean(
ptu.get_numpy(policy_loss))
self.eval_statistics.update(
create_stats_ordered_dict(
'V1 Predictions',
ptu.get_numpy(v1_pred),
))
self.eval_statistics.update(
create_stats_ordered_dict(
'V2 Predictions',
ptu.get_numpy(v2_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
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']
"""
Critic operations.
"""
next_actions = self.target_policy(next_obs)
noise = torch.normal(
torch.zeros_like(next_actions),
self.target_policy_noise,
)
noise = torch.clamp(noise, -self.target_policy_noise_clip,
self.target_policy_noise_clip)
noisy_next_actions = next_actions + noise
target_q1_values = self.target_qf1(next_obs, noisy_next_actions)
target_q2_values = self.target_qf2(next_obs, noisy_next_actions)
target_q_values = torch.min(target_q1_values, target_q2_values)
q_target = rewards + (1. - terminals) * self.discount * target_q_values
q_target = q_target.detach()
q1_pred = self.qf1(obs, actions)
bellman_errors_1 = (q1_pred - q_target)**2
qf1_loss = bellman_errors_1.mean()
q2_pred = self.qf2(obs, actions)
bellman_errors_2 = (q2_pred - q_target)**2
qf2_loss = bellman_errors_2.mean()
"""
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()
policy_actions = policy_loss = None
if self._n_train_steps_total % self.policy_and_target_update_period == 0:
policy_actions = self.policy(obs)
q_output = self.qf1(obs, policy_actions)
policy_loss = -q_output.mean()
self.policy_optimizer.zero_grad()
policy_loss.backward()
self.policy_optimizer.step()
ptu.soft_update_from_to(self.policy, self.target_policy, self.tau)
ptu.soft_update_from_to(self.qf1, self.target_qf1, self.tau)
ptu.soft_update_from_to(self.qf2, self.target_qf2, self.tau)
if self.eval_statistics is None:
"""
Eval should set this to None.
This way, these statistics are only computed for one batch.
"""
if policy_loss is None:
policy_actions = self.policy(obs)
q_output = self.qf1(obs, policy_actions)
policy_loss = -q_output.mean()
self.eval_statistics = OrderedDict()
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(
'Bellman Errors 1',
ptu.get_numpy(bellman_errors_1),
))
self.eval_statistics.update(
create_stats_ordered_dict(
'Bellman Errors 2',
ptu.get_numpy(bellman_errors_2),
))
self.eval_statistics.update(
create_stats_ordered_dict(
'Policy Action',
ptu.get_numpy(policy_actions),
))
def get_np_prediction(model, state, action):
state = ptu.np_to_var(np.expand_dims(state, 0))
action = ptu.np_to_var(np.expand_dims(action, 0))
delta = model(state, action)
return ptu.get_numpy(delta.squeeze(0))
def train_from_torch(self, batch):
rewards = batch['rewards']
terminals = batch['terminals']
obs = batch['observations']
actions = batch['actions']
next_obs = batch['next_observations']
gt.stamp('preback_start', unique=False)
"""
Update QF
"""
with torch.no_grad():
next_actions = self.target_policy(next_obs)
noise = ptu.randn(next_actions.shape) * self.target_policy_noise
noise = torch.clamp(noise, -self.target_policy_noise_clip,
self.target_policy_noise_clip)
noisy_next_actions = torch.clamp(next_actions + noise,
-self.max_action, self.max_action)
next_tau, next_tau_hat, next_presum_tau = self.get_tau(
next_obs, noisy_next_actions, fp=self.target_fp)
target_z1_values = self.target_zf1(next_obs, noisy_next_actions,
next_tau_hat)
target_z2_values = self.target_zf2(next_obs, noisy_next_actions,
next_tau_hat)
target_z_values = torch.min(target_z1_values, target_z2_values)
z_target = self.reward_scale * rewards + (
1. - terminals) * self.discount * target_z_values
tau, tau_hat, presum_tau = self.get_tau(obs, actions, fp=self.fp)
z1_pred = self.zf1(obs, actions, tau_hat)
z2_pred = self.zf2(obs, actions, tau_hat)
zf1_loss = self.zf_criterion(z1_pred, z_target, tau_hat,
next_presum_tau)
zf2_loss = self.zf_criterion(z2_pred, z_target, tau_hat,
next_presum_tau)
gt.stamp('preback_zf', unique=False)
self.zf1_optimizer.zero_grad()
zf1_loss.backward()
self.zf1_optimizer.step()
gt.stamp('backward_zf1', unique=False)
self.zf2_optimizer.zero_grad()
zf2_loss.backward()
self.zf2_optimizer.step()
gt.stamp('backward_zf2', unique=False)
"""
Update FP
"""
if self.tau_type == 'fqf':
with torch.no_grad():
dWdtau = 0.5 * (2 * self.zf1(obs, actions, tau[:, :-1]) -
z1_pred[:, :-1] - z1_pred[:, 1:] +
2 * self.zf2(obs, actions, tau[:, :-1]) -
z2_pred[:, :-1] - z2_pred[:, 1:])
dWdtau /= dWdtau.shape[0] # (N, T-1)
gt.stamp('preback_fp', unique=False)
self.fp_optimizer.zero_grad()
tau[:, :-1].backward(gradient=dWdtau)
self.fp_optimizer.step()
gt.stamp('backward_fp', unique=False)
"""
Policy Loss
"""
policy_actions = self.policy(obs)
risk_param = self.risk_schedule(self._n_train_steps_total)
if self.risk_type == 'VaR':
tau_ = ptu.ones_like(rewards) * risk_param
q_new_actions = self.zf1(obs, policy_actions, tau_)
else:
with torch.no_grad():
new_tau, new_tau_hat, new_presum_tau = self.get_tau(
obs, policy_actions, fp=self.fp)
z_new_actions = self.zf1(obs, policy_actions, new_tau_hat)
if self.risk_type in ['neutral', 'std']:
q_new_actions = torch.sum(new_presum_tau * z_new_actions,
dim=1,
keepdims=True)
if self.risk_type == 'std':
q_std = new_presum_tau * (z_new_actions -
q_new_actions).pow(2)
q_new_actions -= risk_param * q_std.sum(
dim=1, keepdims=True).sqrt()
else:
with torch.no_grad():
risk_weights = distortion_de(new_tau_hat, self.risk_type,
risk_param)
q_new_actions = torch.sum(risk_weights * new_presum_tau *
z_new_actions,
dim=1,
keepdims=True)
policy_loss = -q_new_actions.mean()
gt.stamp('preback_policy', unique=False)
if self._n_train_steps_total % self.policy_and_target_update_period == 0:
self.policy_optimizer.zero_grad()
policy_loss.backward()
policy_grad = ptu.fast_clip_grad_norm(self.policy.parameters(),
self.clip_norm)
self.policy_optimizer.step()
gt.stamp('backward_policy', unique=False)
ptu.soft_update_from_to(self.policy, self.target_policy,
self.soft_target_tau)
ptu.soft_update_from_to(self.zf1, self.target_zf1,
self.soft_target_tau)
ptu.soft_update_from_to(self.zf2, self.target_zf2,
self.soft_target_tau)
if self.tau_type == 'fqf':
ptu.soft_update_from_to(self.fp, self.target_fp,
self.soft_target_tau)
gt.stamp('soft_update', unique=False)
"""
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.
"""
self.eval_statistics['ZF1 Loss'] = zf1_loss.item()
self.eval_statistics['ZF2 Loss'] = zf2_loss.item()
self.eval_statistics['Policy Loss'] = policy_loss.item()
self.eval_statistics['Policy Grad'] = policy_grad
self.eval_statistics.update(
create_stats_ordered_dict(
'Z1 Predictions',
ptu.get_numpy(z1_pred),
))
self.eval_statistics.update(
create_stats_ordered_dict(
'Z2 Predictions',
ptu.get_numpy(z2_pred),
))
self.eval_statistics.update(
create_stats_ordered_dict(
'Z Targets',
ptu.get_numpy(z_target),
))
self.eval_statistics.update(
create_stats_ordered_dict(
'Policy Action',
ptu.get_numpy(policy_actions),
))
self._n_train_steps_total += 1
def train_from_torch(self, batch):
rewards = batch['rewards']
terminals = batch['terminals']
obs = batch['observations']
actions = batch['actions']
next_obs = batch['next_observations']
gt.stamp('preback_start', unique=False)
"""
Update Alpha
"""
new_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 = self.alpha
"""
Update QF
"""
with torch.no_grad():
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
q1_pred = self.qf1(obs, actions)
q2_pred = self.qf2(obs, actions)
qf1_loss = self.qf_criterion(q1_pred, q_target)
qf2_loss = self.qf_criterion(q2_pred, q_target)
gt.stamp('preback_qf', unique=False)
self.qf1_optimizer.zero_grad()
qf1_loss.backward()
self.qf1_optimizer.step()
gt.stamp('backward_qf1', unique=False)
self.qf2_optimizer.zero_grad()
qf2_loss.backward()
self.qf2_optimizer.step()
gt.stamp('backward_qf2', unique=False)
"""
Update Policy
"""
q_new_actions = torch.min(
self.qf1(obs, new_actions),
self.qf2(obs, new_actions),
)
policy_loss = (alpha * log_pi - q_new_actions).mean()
gt.stamp('preback_policy', unique=False)
self.policy_optimizer.zero_grad()
policy_loss.backward()
policy_grad = ptu.fast_clip_grad_norm(self.policy.parameters(),
self.clip_norm)
self.policy_optimizer.step()
gt.stamp('backward_policy', unique=False)
"""
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
self.eval_statistics['QF1 Loss'] = qf1_loss.item()
self.eval_statistics['QF2 Loss'] = qf2_loss.item()
self.eval_statistics['Policy Loss'] = policy_loss.item()
self.eval_statistics['Policy Grad'] = policy_grad
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
def _do_policy_training(self, epoch):
if self.policy_optim_batch_size_from_expert > 0:
policy_batch_from_policy_buffer = self.get_batch(
self.policy_optim_batch_size -
self.policy_optim_batch_size_from_expert, False)
policy_batch_from_expert_buffer = self.get_batch(
self.policy_optim_batch_size_from_expert, True)
policy_batch = {}
for k in policy_batch_from_policy_buffer:
policy_batch[k] = torch.cat([
policy_batch_from_policy_buffer[k],
policy_batch_from_expert_buffer[k]
],
dim=0)
else:
policy_batch = self.get_batch(self.policy_optim_batch_size, False)
obs = policy_batch['observations'] / self.rescale
obs = torch.index_select(obs, 1, self.state_indices)
if self.wrap_absorbing:
pass
# obs = torch.cat([obs, policy_batch['absorbing'][:, 0:1]], dim=-1)
else:
self.ebm.eval()
ebm_input = obs
ebm_rew = self.get_energy(ebm_input).detach()
if self.clamp_magnitude > 0:
print(self.clamp_magnitude, self.clamp_magnitude is None)
ebm_rew = torch.clamp(ebm_rew,
min=-1.0 * self.clamp_magnitude,
max=self.clamp_magnitude)
# compute the reward using the algorithm
shape_reward = torch.index_select(
obs, 1,
torch.LongTensor([0]).to(ptu.device)
) # torch.index_select(policy_batch['actions'], 1, torch.LongTensor([0]).to(ptu.device))
policy_batch['rewards'] = reward_func(
self.rew_func, self.cons)(ebm_rew) * shape_reward
# print('ebm_obs: ', np.mean(ptu.get_numpy(policy_batch['observations']), axis=0))
# print('ebm: ', np.mean(ptu.get_numpy(ebm_rew)))
# print('ebm_rew: ', np.mean(ptu.get_numpy(policy_batch['rewards'])))
# buffer = self.target_state_buffer
# exp_obs = buffer[np.random.choice(buffer.shape[0], size=100)]
# exp_input = torch.Tensor(exp_obs / self.rescale).to(ptu.device)
# exp_ebm = self.get_energy(exp_input).detach()
# exp_rew = reward_func(self.rew_func, self.cons)(exp_ebm)
# print('exp_obs: ', np.mean(exp_obs,axis=0))
# print("exp data ebm: ", np.mean(ptu.get_numpy(exp_ebm)))
# print("exp data rew: ", np.mean(ptu.get_numpy(exp_rew)))
# policy optimization step
self.policy_trainer.train_step(policy_batch)
"""
Save some statistics for eval
"""
if self.ebm_eval_statistics is None:
"""
Eval should set this to None.
This way, these statistics are only computed for one batch.
"""
self.ebm_eval_statistics = OrderedDict()
self.ebm_eval_statistics['ebm Rew Mean'] = np.mean(
ptu.get_numpy(policy_batch['rewards']))
self.ebm_eval_statistics['ebm Rew Std'] = np.std(
ptu.get_numpy(policy_batch['rewards']))
self.ebm_eval_statistics['ebm Rew Max'] = np.max(
ptu.get_numpy(policy_batch['rewards']))
self.ebm_eval_statistics['ebm Rew Min'] = np.min(
ptu.get_numpy(policy_batch['rewards']))
def decode_one_np(self, inputs, cont=True):
return np.clip(
ptu.get_numpy(
self.decode(ptu.from_numpy(inputs).reshape(1, -1),
cont=cont))[0], 0, 1)
def encode_np(self, inputs, cont=True):
return ptu.get_numpy(self.encode(ptu.from_numpy(inputs), cont=cont))
def sample_np(self, batch_size):
latents = np.random.normal(size=(batch_size, self.latent_dim))
latents_torch = ptu.from_numpy(latents)
return ptu.get_numpy(self.decode(latents_torch))