"""
Value Decomposition Networks (VDN)
Paper link:
https://arxiv.org/pdf/1706.05296.pdf
Implementation: MindSpore
"""
from mindspore.nn import MSELoss
from xuance.mindspore import ms, Module, Tensor, optim, ops
from xuance.mindspore.learners import LearnerMAS
from xuance.mindspore.utils import clip_grads
from xuance.common import List
from argparse import Namespace
from operator import itemgetter
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class VDN_Learner(LearnerMAS):
def __init__(self,
config: Namespace,
model_keys: List[str],
agent_keys: List[str],
policy: Module,
callback):
super(VDN_Learner, self).__init__(config, model_keys, agent_keys, policy, callback)
self.optimizer = optim.Adam(params=self.policy.trainable_params(), lr=config.learning_rate, eps=1e-5)
self.scheduler = optim.lr_scheduler.LinearLR(self.optimizer, start_factor=1.0, end_factor=self.end_factor_lr_decay,
total_iters=self.config.running_steps)
self.gamma = config.gamma
self.sync_frequency = config.sync_frequency
self.mse_loss = MSELoss()
self.n_actions = {k: self.policy.action_space[k].n for k in self.model_keys}
# Get gradient function
self.grad_fn = ms.value_and_grad(self.forward_fn, None, self.optimizer.parameters, has_aux=True)
self.policy.set_train()
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def forward_fn(self, obs, actions, agt_mask, avail_actions, ids, q_tot_target):
_, _, q_eval = self.policy(observation=obs, agent_ids=ids, avail_actions=avail_actions)
q_eval_a = {}
for key in self.model_keys:
q_eval_a[key] = q_eval[key].gather(actions[key].unsqueeze(-1).astype(ms.int32), -1, -1).reshape(-1)
q_eval_a[key] *= agt_mask[key]
q_tot_eval = self.policy.Q_tot(q_eval_a)
loss = self.mse_loss(logits=q_tot_eval, labels=q_tot_target)
return loss, q_tot_eval
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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)
batch_size = sample_Tensor['batch_size']
obs = sample_Tensor['obs']
actions = sample_Tensor['actions']
obs_next = sample_Tensor['obs_next']
rewards = sample_Tensor['rewards']
terminals = sample_Tensor['terminals']
agent_mask = sample_Tensor['agent_mask']
avail_actions = sample_Tensor['avail_actions']
avail_actions_next = sample_Tensor['avail_actions_next']
IDs = sample_Tensor['agent_ids']
if self.use_parameter_sharing:
key = self.model_keys[0]
bs = batch_size * self.n_agents
rewards_tot = rewards[key].mean(axis=1).reshape(batch_size, 1)
terminals_tot = terminals[key].all(axis=1).astype(ms.float32).reshape(batch_size, 1)
else:
bs = batch_size
rewards_tot = ops.stack(itemgetter(*self.agent_keys)(rewards), axis=1).mean(axis=-1).reshape(batch_size, 1)
terminals_tot = ops.stack(itemgetter(*self.agent_keys)(terminals),
axis=1).all(axis=1).astype(ms.float32).reshape(batch_size, 1)
info = self.callback.on_update_start(self.iterations, method="update",
policy=self.policy, sample_Tensor=sample_Tensor, bs=bs,
rewards_tot=rewards_tot, terminals_tot=terminals_tot)
_, q_next = self.policy.Qtarget(observation=obs_next, agent_ids=IDs)
q_next_a = {}
for key in self.model_keys:
mask_values = agent_mask[key]
if self.use_actions_mask:
q_next[key][avail_actions_next[key] == 0] = -1e10
if self.config.double_q:
_, act_next, _ = self.policy(observation=obs_next, agent_ids=IDs,
avail_actions=avail_actions, agent_key=key)
q_next_a[key] = q_next[key].gather(act_next[key].astype(ms.int32).unsqueeze(-1), -1, -1).reshape(bs)
else:
q_next_a[key] = q_next[key].max(axis=-1, keepdim=True).values.reshape(bs)
q_next_a[key] *= mask_values
info.update(self.callback.on_update_agent_wise(self.iterations, key, info=info, method="update",
mask_values=mask_values,
q_next_a=q_next_a))
q_tot_next = self.policy.Qtarget_tot(q_next_a)
q_tot_target = rewards_tot + (1 - terminals_tot) * self.gamma * q_tot_next
# calculate the loss function
(loss, q_tot_eval), grads = self.grad_fn(obs, actions, agent_mask, avail_actions, IDs, q_tot_target)
if self.use_grad_clip:
grads = clip_grads(grads, Tensor(-self.grad_clip_norm), Tensor(self.grad_clip_norm))
self.optimizer(grads)
self.scheduler.step()
lr = self.scheduler.get_last_lr()[0]
info.update({
"learning_rate": lr.asnumpy(),
"loss_Q": loss.asnumpy(),
"predictQ": q_tot_eval.mean().asnumpy()
})
if self.iterations % self.sync_frequency == 0:
self.policy.copy_target()
info.update(self.callback.on_update_end(self.iterations, method="update", policy=self.policy, info=info,
q_tot_eval=q_tot_eval, q_tot_next=q_tot_next,
q_tot_target=q_tot_target))
return info