"""
Value-Dcomposition Actor-Critic (VDAC)
Paper link:
https://ojs.aaai.org/index.php/AAAI/article/view/17353
Implementation: Pytorch
"""
import torch
from torch import nn
from argparse import Namespace
from operator import itemgetter
from xuance.common import List
from xuance.torch.learners.multi_agent_rl.iac_learner import IAC_Learner
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class VDAC_Learner(IAC_Learner):
def __init__(self,
config: Namespace,
model_keys: List[str],
agent_keys: List[str],
policy: nn.Module,
callback):
super(VDAC_Learner, self).__init__(config, model_keys, agent_keys, policy, callback)
self.use_global_state = True if config.mixer == "QMIX" else getattr(config, "use_global_state", False)
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def update(self, sample):
self.iterations += 1
# prepare training data
sample_Tensor = self.build_training_data(sample=sample,
use_parameter_sharing=self.use_parameter_sharing,
use_actions_mask=self.use_actions_mask,
use_global_state=self.use_global_state)
batch_size = sample_Tensor['batch_size']
state = sample_Tensor['state']
obs = sample_Tensor['obs']
actions = sample_Tensor['actions']
agent_mask = sample_Tensor['agent_mask']
avail_actions = sample_Tensor['avail_actions']
values = sample_Tensor['values']
returns = sample_Tensor['returns']
advantages = sample_Tensor['advantages']
IDs = sample_Tensor['agent_ids']
bs = batch_size * self.n_agents if self.use_parameter_sharing else batch_size
info = self.callback.on_update_start(self.iterations, method="update",
policy=self.policy, sample_Tensor=sample_Tensor, bs=bs)
# feedforward
_, pi_dist_dict = self.policy(observation=obs, agent_ids=IDs, avail_actions=avail_actions)
_, values_pred_individual = self.policy.get_values(observation=obs, agent_ids=IDs)
if self.use_parameter_sharing:
values_n = values_pred_individual[self.model_keys[0]].reshape(batch_size, self.n_agents)
else:
values_n = self.get_joint_input(values_pred_individual)
if self.config.mixer == "VDN":
values_tot = self.policy.value_tot(values_n)
elif self.config.mixer == "QMIX":
values_tot = self.policy.value_tot(values_n, state)
else:
raise NotImplementedError("Mixer not implemented.")
if self.use_parameter_sharing:
values_tot = values_tot.reshape(batch_size, 1).repeat(1, self.n_agents).reshape(bs)
values_pred_dict = {k: values_tot for k in self.model_keys}
loss_a, loss_e, loss_c = [], [], []
for key in self.model_keys:
mask_values = agent_mask[key]
# policy gradient loss
log_pi = pi_dist_dict[key].log_prob(actions[key])
pg_loss = -((advantages[key].detach() * log_pi) * mask_values).sum() / mask_values.sum()
loss_a.append(pg_loss)
# entropy loss
entropy = pi_dist_dict[key].entropy()
entropy_loss = (entropy * mask_values).sum() / mask_values.sum()
loss_e.append(entropy_loss)
# value loss
value_pred_i = values_pred_dict[key].reshape(bs)
value_target = returns[key].reshape(bs)
values_i = values[key].reshape(bs)
if self.use_value_clip:
value_clipped = values_i + (value_pred_i - values_i).clamp(-self.value_clip_range,
self.value_clip_range)
if self.use_value_norm:
self.value_normalizer[key].update(value_target.reshape(bs, 1))
value_target = self.value_normalizer[key].normalize(value_target.reshape(bs, 1)).reshape(bs)
if self.use_huber_loss:
loss_v = self.huber_loss(value_pred_i, value_target)
loss_v_clipped = self.huber_loss(value_clipped, value_target)
else:
loss_v = (value_pred_i - value_target) ** 2
loss_v_clipped = (value_clipped - value_target) ** 2
loss_c_ = torch.max(loss_v, loss_v_clipped) * mask_values
loss_c.append(loss_c_.sum() / mask_values.sum())
else:
if self.use_value_norm:
self.value_normalizer[key].update(value_target)
value_target = self.value_normalizer[key].normalize(value_target)
if self.use_huber_loss:
loss_v = self.huber_loss(value_pred_i, value_target) * mask_values
else:
loss_v = ((value_pred_i - value_target) ** 2) * mask_values
loss_c.append(loss_v.sum() / mask_values.sum())
info.update(self.callback.on_update_agent_wise(self.iterations, key, info=info, method="update",
mask_values=mask_values, log_pi=log_pi, pg_loss=pg_loss,
entropy=entropy, entropy_loss=entropy_loss,
value_pred_i=value_pred_i, value_target=value_target,
values_i=values_i, loss_v=loss_v))
# Total loss
loss = sum(loss_a) + self.vf_coef * sum(loss_c) - self.ent_coef * sum(loss_e)
self.optimizer.zero_grad()
loss.backward()
if self.use_grad_clip:
grad_norm = torch.nn.utils.clip_grad_norm_(self.policy.parameters_model, self.grad_clip_norm)
info["gradient_norm"] = grad_norm.item()
self.optimizer.step()
if self.scheduler is not None:
self.scheduler.step()
# Logger
lr = self.optimizer.state_dict()['param_groups'][0]['lr']
info.update({
"learning_rate": lr,
"pg_loss": sum(loss_a).item(),
"vf_loss": sum(loss_c).item(),
"entropy_loss": sum(loss_e).item(),
"loss": loss.item(),
"predict_value": values_tot.mean().item()
})
info.update(self.callback.on_update_end(self.iterations, method="update", policy=self.policy, info=info))
return info
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def update_rnn(self, sample):
self.iterations += 1
sample_Tensor = self.build_training_data(sample=sample,
use_parameter_sharing=self.use_parameter_sharing,
use_actions_mask=self.use_actions_mask,
use_global_state=self.use_global_state)
batch_size = sample_Tensor['batch_size']
state = sample_Tensor['state']
bs_rnn = batch_size * self.n_agents if self.use_parameter_sharing else batch_size
obs = sample_Tensor['obs']
actions = sample_Tensor['actions']
values = sample_Tensor['values']
returns = sample_Tensor['returns']
advantages = sample_Tensor['advantages']
avail_actions = sample_Tensor['avail_actions']
agent_mask = sample_Tensor['agent_mask']
filled = sample_Tensor['filled']
seq_len = filled.shape[1]
IDs = sample_Tensor['agent_ids']
if self.use_parameter_sharing:
filled = filled.unsqueeze(1).expand(batch_size, self.n_agents, seq_len).reshape(bs_rnn, seq_len)
info = self.callback.on_update_start(self.iterations, method="update_rnn",
policy=self.policy, sample_Tensor=sample_Tensor, bs_rnn=bs_rnn)
rnn_hidden_actor = {k: self.policy.actor_representation[k].init_hidden(bs_rnn) for k in self.model_keys}
rnn_hidden_critic = {k: self.policy.critic_representation[k].init_hidden(bs_rnn) for k in self.model_keys}
# feedforward
_, pi_dist_dict = self.policy(obs, agent_ids=IDs, avail_actions=avail_actions, rnn_hidden=rnn_hidden_actor)
_, values_pred_individual = self.policy.get_values(obs, agent_ids=IDs, rnn_hidden=rnn_hidden_critic)
if self.use_parameter_sharing:
values_n = values_pred_individual[self.model_keys[0]].reshape(
batch_size, self.n_agents, seq_len).transpose(1, 2).reshape(-1, self.n_agents)
else:
if self.n_agents == 1:
values_n = values_pred_individual[self.agent_keys[0]].reshape(-1, self.n_agents)
else:
values_n = torch.stack(itemgetter(*self.agent_keys)(values_pred_individual),
dim=2).reshape(-1, self.n_agents)
if self.config.mixer == "VDN":
values_tot = self.policy.value_tot(values_n)
elif self.config.mixer == "QMIX":
values_tot = self.policy.value_tot(values_n, state)
else:
raise NotImplementedError("Mixer not implemented.")
if self.use_parameter_sharing:
values_tot = values_tot.reshape(batch_size, 1, seq_len).repeat(1, self.n_agents, 1)
else:
values_tot = values_tot.reshape(batch_size, seq_len)
values_pred_dict = {k: values_tot for k in self.model_keys}
# calculate losses for each agent
loss_a, loss_e, loss_c = [], [], []
for key in self.model_keys:
mask_values = agent_mask[key] * filled
# policy gradient loss
log_pi = pi_dist_dict[key].log_prob(actions[key]).reshape(bs_rnn, seq_len)
pg_loss = -((advantages[key].detach() * log_pi) * mask_values).sum() / mask_values.sum()
loss_a.append(pg_loss)
# entropy loss
entropy = pi_dist_dict[key].entropy()
entropy_loss = (entropy * mask_values).sum() / mask_values.sum()
loss_e.append(entropy_loss)
# value loss
value_pred_i = values_pred_dict[key].reshape(bs_rnn, seq_len)
value_target = returns[key].reshape(bs_rnn, seq_len)
values_i = values[key].reshape(bs_rnn, seq_len)
if self.use_value_clip:
value_clipped = values_i + (value_pred_i - values_i).clamp(-self.value_clip_range,
self.value_clip_range)
if self.use_value_norm:
self.value_normalizer[key].update(value_target.reshape(-1, 1))
value_target = self.value_normalizer[key].normalize(value_target.reshape(-1, 1))
value_target = value_target.reshape(bs_rnn, seq_len)
if self.use_huber_loss:
loss_v = self.huber_loss(value_pred_i, value_target)
loss_v_clipped = self.huber_loss(value_clipped, value_target)
else:
loss_v = (value_pred_i - value_target) ** 2
loss_v_clipped = (value_clipped - value_target) ** 2
loss_c_ = torch.max(loss_v, loss_v_clipped) * mask_values
loss_c.append(loss_c_.sum() / mask_values.sum())
else:
if self.use_value_norm:
self.value_normalizer[key].update(value_target)
value_target = self.value_normalizer[key].normalize(value_target)
if self.use_huber_loss:
loss_v = self.huber_loss(value_pred_i, value_target)
else:
loss_v = (value_pred_i - value_target) ** 2
loss_c.append((loss_v * mask_values).sum() / mask_values.sum())
info.update(self.callback.on_update_agent_wise(self.iterations, key, info=info, method="update_rnn",
mask_values=mask_values, log_pi=log_pi,
pg_loss=pg_loss, entropy=entropy,
entropy_loss=entropy_loss, value_pred_i=value_pred_i,
value_target=value_target, values_i=values_i,
loss_v=loss_v))
loss = sum(loss_a) + self.vf_coef * sum(loss_c) - self.ent_coef * sum(loss_e)
self.optimizer.zero_grad()
loss.backward()
if self.use_grad_clip:
grad_norm = torch.nn.utils.clip_grad_norm_(self.policy.parameters_model, self.grad_clip_norm)
info["gradient_norm"] = grad_norm.item()
self.optimizer.step()
if self.scheduler is not None:
self.scheduler.step()
# Logger
lr = self.optimizer.state_dict()['param_groups'][0]['lr']
info.update({
"learning_rate": lr,
"pg_loss": sum(loss_a).item(),
"vf_loss": sum(loss_c).item(),
"entropy_loss": sum(loss_e).item(),
"loss": loss.item(),
"predict_value": values_tot.mean().item()
})
info.update(self.callback.on_update_end(self.iterations, method="update_rnn", policy=self.policy, info=info))
return info