"""
Value Decomposition Actor-Critic (VDAC)
Paper link: https://ojs.aaai.org/index.php/AAAI/article/view/17353
Implementation: MindSpore
"""
from argparse import Namespace
from xuance.common import List
from xuance.mindspore import ops, Module, Tensor
from xuance.mindspore.utils import clip_grads
from xuance.mindspore.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: 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 forward_fn(self, *args):
bs, batch_size, state, obs, actions, agent_mask, avail_actions, values, returns, advantages, IDs = args
info_forward = {}
pi_dist_mu, pi_dist_std, pi_dist_logits = {}, {}, {}
# feedforward
if self.is_continuous:
_, pi_dist_mu, pi_dist_std = self.policy(observation=obs, agent_ids=IDs, avail_actions=avail_actions)
else:
_, pi_dist_logits = 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 = ops.repeat_elements(values_tot.reshape(batch_size, 1), rep=self.n_agents, axis=1).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
if self.is_continuous:
log_pi = self.pi_dist[key]._log_prob(value=actions[key], mean=pi_dist_mu[key], sd=pi_dist_std[key])
log_pi = ops.reduce_sum(x=log_pi, axis=-1)
entropy = self.pi_dist[key]._entropy(mean=pi_dist_mu[key], sd=pi_dist_std[key])
entropy = ops.reduce_sum(x=entropy, axis=-1)
else:
probs = self.softmax(pi_dist_logits[key])
log_pi = self.pi_dist[key]._log_prob(value=actions[key], probs=probs)
entropy = self.pi_dist[key].entropy(probs=probs)
pg_loss = -(ops.stop_gradient(advantages[key]) * log_pi * mask_values).sum() / mask_values.sum()
loss_a.append(pg_loss)
# entropy loss
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)
value_target = ops.stop_gradient(value_target)
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_ = ops.maximum(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)
value_target = ops.stop_gradient(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_forward.update({
f"predict_value/{key}": value_pred_i.mean().asnumpy()
})
info_forward.update(self.callback.on_update_agent_wise(self.iterations, key, info=info_forward,
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)
return loss, sum(loss_a), sum(loss_e), sum(loss_c), info_forward
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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
(loss, loss_a, loss_e, loss_c, info_forward), grads = self.grad_fn(bs, batch_size, state, obs, actions,
agent_mask, avail_actions, values, returns,
advantages, IDs)
if self.use_grad_clip:
grads = clip_grads(grads, Tensor(-self.grad_clip_norm), Tensor(self.grad_clip_norm))
self.optimizer(grads) # backpropagation
self.scheduler.step() # update learning rate
lr = self.scheduler.get_last_lr()[0]
info.update({
"learning_rate": lr.asnumpy(),
"pg_loss": loss_a.asnumpy(),
"vf_loss": loss_c.asnumpy(),
"entropy_loss": loss_e.asnumpy(),
"loss": loss.asnumpy(),
})
info.update(info_forward)
info.update(self.callback.on_update_end(self.iterations, method="update", policy=self.policy, info=info))
return info