"""
Value Decomposition Actor-Critic (VDAC)
Paper link: https://ojs.aaai.org/index.php/AAAI/article/view/17353
Implementation: TensorFlow2
"""
import numpy as np
from argparse import Namespace
from operator import itemgetter
from xuance.common import List
from xuance.tensorflow import tk, Module
from xuance.tensorflow import tf
from xuance.tensorflow.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)
# @tf.function
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def forward_fn(self, *args):
batch_size, bs, state, obs, actions, agent_mask, avail_actions, values, returns, advantages, IDs = args
with tf.GradientTape() as tape:
loss_a, loss_e, loss_c = [], [], []
if self.is_continuous:
_, pi_mu, pi_std = self.policy(observation=obs, agent_ids=IDs, avail_actions=avail_actions)
for key in self.model_keys:
mask_values = agent_mask[key]
mask_values_sum = tf.reduce_sum(mask_values)
log_2pi = tf.math.log(2.0 * np.pi)
# policy gradient loss
log_std = tf.math.log(pi_std[key] + 1e-8)
log_prob = -0.5 * (
((actions[key] - pi_mu[key]) / (pi_std[key] + 1e-8)) ** 2 + 2.0 * log_std + log_2pi)
log_pi = tf.reduce_sum(log_prob, axis=-1, keepdims=False)
pg_loss = -tf.reduce_sum((advantages[key] * log_pi) * mask_values) / mask_values_sum
loss_a.append(pg_loss)
# entropy loss
entropy = tf.reduce_sum(0.5 + 0.5 * log_2pi + log_std, axis=-1, keepdims=True)
entropy_loss = tf.reduce_sum(entropy * mask_values) / mask_values_sum
loss_e.append(entropy_loss)
else:
_, pi_logits = self.policy(observation=obs, agent_ids=IDs, avail_actions=avail_actions)
for key in self.model_keys:
mask_values = agent_mask[key]
mask_values_sum = tf.reduce_sum(mask_values)
# policy gradient loss
log_prob = tf.nn.log_softmax(pi_logits[key], axis=-1)
log_pi = tf.gather(log_prob, actions[key], axis=-1, batch_dims=-1)
log_pi = tf.squeeze(log_pi, axis=-1)
pg_loss = -tf.reduce_sum((advantages[key] * log_pi) * mask_values) / mask_values_sum
loss_a.append(pg_loss)
# entropy loss
probs = tf.exp(log_prob)
entropy = -tf.reduce_sum(probs * log_prob, axis=-1, keepdims=False)
entropy_loss = tf.reduce_sum(entropy * mask_values) / mask_values_sum
loss_e.append(entropy_loss)
_, values_pred_individual = self.policy.get_values(observation=obs, agent_ids=IDs)
if self.use_parameter_sharing:
values_n = tf.reshape(values_pred_individual[self.model_keys[0]], [batch_size, self.n_agents])
else:
if self.n_agents == 1:
values_n = itemgetter(*self.agent_keys)(values_pred_individual)
else:
values_n = tf.concat(itemgetter(*self.agent_keys)(values_pred_individual), aixs=-1)
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 = tf.reshape(tf.tile(tf.reshape(values_tot, [batch_size, 1]), [1, self.n_agents]), [bs])
values_pred_dict = {k: values_tot for k in self.model_keys}
for key in self.model_keys:
# value loss
value_pred_i = tf.reshape(values_pred_dict[key], [bs])
value_target = tf.reshape(returns[key], [bs])
values_i = tf.reshape(values[key], [bs])
if self.use_value_clip:
value_clipped = values_i + tf.clip_by_value(value_pred_i - values_i,
-self.value_clip_range, self.value_clip_range)
if self.use_value_norm:
self.value_normalizer[key].update(tf.reshape(value_target, [bs, 1]))
value_target = tf.reshape(self.value_normalizer[key].normalize(tf.reshape(value_target,
[bs, 1])), [bs])
if self.use_huber_loss:
loss_v = tk.losses.huber(value_target, value_pred_i, self.huber_delta)
loss_v_clipped = tk.losses.huber(value_target, value_clipped, self.huber_delta)
else:
loss_v = (value_pred_i - value_target) ** 2
loss_v_clipped = (value_clipped - value_target) ** 2
loss_c_ = tf.maximum(loss_v, loss_v_clipped) * mask_values
loss_c.append(tf.reduce_sum(loss_c_) / 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 = tk.losses.huber(value_target, value_pred_i, self.huber_delta) * mask_values
else:
loss_v = ((value_pred_i - value_target) ** 2) * mask_values
loss_c.append(tf.reduce_sum(loss_v) / mask_values_sum)
# Total loss
loss = sum(loss_a) + self.vf_coef * sum(loss_c) - self.ent_coef * sum(loss_e)
gradients = tape.gradient(loss, self.policy.trainable_variables)
if self.use_grad_clip:
gradients, _ = tf.clip_by_global_norm(gradients, clip_norm=self.grad_clip_norm)
self.optimizer.apply_gradients(zip(gradients, self.policy.trainable_variables))
else:
self.optimizer.apply_gradients(zip(gradients, self.policy.trainable_variables))
return loss, loss_a, loss_c, loss_e, values_pred_dict
# @tf.function
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def learn(self, *inputs):
if self.distributed_training:
loss, a_loss, c_loss, e_loss, v_pred = self.policy.mirrored_strategy.run(self.forward_fn, args=inputs)
return (self.policy.mirrored_strategy.reduce(tf.distribute.ReduceOp.SUM, loss, axis=None),
self.policy.mirrored_strategy.reduce(tf.distribute.ReduceOp.SUM, a_loss, axis=None),
self.policy.mirrored_strategy.reduce(tf.distribute.ReduceOp.SUM, c_loss, axis=None),
self.policy.mirrored_strategy.reduce(tf.distribute.ReduceOp.SUM, e_loss, axis=None),
self.policy.mirrored_strategy.reduce(tf.distribute.ReduceOp.SUM, v_pred, axis=None))
else:
return self.forward_fn(*inputs)
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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)
loss, a_loss, c_loss, e_loss, v_pred = self.learn(batch_size, bs, state, obs, actions, agent_mask,
avail_actions, values, returns, advantages, IDs)
info.update({f"predict_value/{key}": tf.reduce_mean(v_pred[key]).numpy() for key in self.model_keys})
info.update({
# "learning_rate": lr,
"pg_loss": sum(a_loss).numpy(),
"vf_loss": sum(c_loss).numpy(),
"entropy_loss": sum(e_loss).numpy(),
"loss": loss.numpy(),
})
info.update(self.callback.on_update_end(self.iterations, method="update", policy=self.policy, info=info))
return info