"""
Independent Soft Actor-critic (ISAC)
Implementation: TensorFlow 2.X
"""
from argparse import Namespace
from xuance.common import List
from xuance.tensorflow import tf, tk, Module
from xuance.tensorflow.learners import LearnerMAS
from xuance.tensorflow.learners.policy_gradient.sac_learner import AlphaLayer
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class ISAC_Learner(LearnerMAS):
def __init__(self,
config: Namespace,
model_keys: List[str],
agent_keys: List[str],
policy: Module,
callback):
super(ISAC_Learner, self).__init__(config, model_keys, agent_keys, policy, callback)
self.build_optimizer()
self.gamma = config.gamma
self.tau = config.tau
self.alpha = {key: config.alpha for key in self.model_keys}
self.use_automatic_entropy_tuning = config.use_automatic_entropy_tuning
if self.use_automatic_entropy_tuning:
self.target_entropy = {key: -policy.action_space[key].shape[-1] for key in self.model_keys}
self.alpha_layer = {key: AlphaLayer() for key in self.model_keys}
self.alpha = {key: tf.exp(self.alpha_layer[key].log_alpha) for key in self.model_keys}
if ("macOS" in self.os_name) and ("arm" in self.os_name): # For macOS with Apple's M-series chips.
self.alpha_optimizer = {key: tk.optimizers.legacy.Adam(config.learning_rate_actor)
for key in self.model_keys}
else:
self.alpha_optimizer = {key: tk.optimizers.Adam(config.learning_rate_actor) for key in self.model_keys}
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def build_optimizer(self):
if ("macOS" in self.os_name) and ("arm" in self.os_name): # For macOS with Apple's M-series chips.
self.optimizer = {
key: {'actor': tk.optimizers.legacy.Adam(self.config.learning_rate_actor),
'critic': tk.optimizers.legacy.Adam(self.config.learning_rate_critic)}
for key in self.model_keys}
else:
self.optimizer = {
key: {'actor': tk.optimizers.Adam(self.config.learning_rate_actor),
'critic': tk.optimizers.Adam(self.config.learning_rate_critic)}
for key in self.model_keys}
# @tf.function
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def forward_fn(self, *args):
bs, obs, actions, rewards, obs_next, terminals, IDs, agent_mask = args
info_train, gradients_c, gradients_a, gradients_alpha = {}, {}, {}, {}
with tf.GradientTape(persistent=True) as tape:
# Update critic
_, actions_next, log_pi_next = self.policy(observation=obs_next, agent_ids=IDs)
_, _, action_q_1, action_q_2 = self.policy.Qpolicy(observation=obs, actions=actions, agent_ids=IDs)
_, _, next_q = self.policy.Qtarget(next_observation=obs_next, next_actions=actions_next, agent_ids=IDs)
for key in self.model_keys:
mask_values = agent_mask[key]
action_q_1_i, action_q_2_i = tf.reshape(action_q_1[key], [bs]), tf.reshape(action_q_2[key], [bs])
log_pi_next_eval = tf.reshape(log_pi_next[key], [bs])
next_q_i = tf.reshape(next_q[key], [bs])
target_value = next_q_i - self.alpha[key] * log_pi_next_eval
backup = rewards[key] + (1 - terminals[key]) * self.gamma * target_value
backup = tf.stop_gradient(backup)
td_error_1, td_error_2 = action_q_1_i - backup, action_q_2_i - backup
td_error_1 *= mask_values
td_error_2 *= mask_values
loss_c = (tf.reduce_sum(td_error_1 ** 2) + tf.reduce_sum(td_error_2 ** 2)) / tf.reduce_sum(mask_values)
gradients_c[key] = tape.gradient(loss_c, self.policy.critic_trainable_variables(key))
if self.use_grad_clip:
gradients_c[key], _ = tf.clip_by_global_norm(gradients_c[key], clip_norm=self.grad_clip_norm)
self.optimizer[key]['critic'].apply_gradients(zip(gradients_c[key],
self.policy.critic_trainable_variables(key)))
else:
self.optimizer[key]['critic'].apply_gradients(zip(gradients_c[key],
self.policy.critic_trainable_variables(key)))
info_train.update({f"{key}/loss_critic": loss_c})
# Update actor
_, actions_eval, log_pi_eval = self.policy(observation=obs, agent_ids=IDs)
log_pi_eval_i = {}
for key in self.model_keys:
_, _, policy_q_1, policy_q_2 = self.policy.Qpolicy(observation=obs, actions=actions_eval,
agent_ids=IDs, agent_key=key)
log_pi_eval_i[key] = tf.reshape(log_pi_eval[key], [bs])
policy_q = tf.reshape(tf.math.minimum(policy_q_1[key], policy_q_2[key]), [bs])
loss_a = tf.reduce_sum(
(self.alpha[key] * log_pi_eval_i[key] - policy_q) * mask_values) / tf.reduce_sum(
mask_values)
gradients_a[key] = tape.gradient(loss_a, self.policy.actor_trainable_variables(key))
if self.use_grad_clip:
gradients_a[key], _ = tf.clip_by_global_norm(gradients_a[key], clip_norm=self.grad_clip_norm)
self.optimizer[key]['actor'].apply_gradients(zip(gradients_a[key],
self.policy.actor_trainable_variables(key)))
else:
self.optimizer[key]['actor'].apply_gradients(zip(gradients_a[key],
self.policy.actor_trainable_variables(key)))
info_train.update({f"{key}/loss_actor": loss_a,
f"{key}/predictQ": tf.math.reduce_mean(policy_q)})
# Automatic entropy tuning
if self.use_automatic_entropy_tuning:
for key in self.model_keys:
alpha_loss = -tf.math.reduce_mean(
self.alpha_layer[key].log_alpha.value() * (log_pi_eval_i[key] + self.target_entropy[key]))
gradients_alpha[key] = tape.gradient(alpha_loss, self.alpha_layer[key].trainable_variables)
gradients_alpha[key], _ = tf.clip_by_global_norm(gradients_alpha[key],
clip_norm=self.grad_clip_norm)
self.alpha_optimizer[key].apply_gradients(zip(gradients_alpha[key],
self.alpha_layer[key].trainable_variables))
self.alpha[key] = tf.math.exp(self.alpha_layer[key].log_alpha)
info_train.update({f"{key}/alpha_loss": alpha_loss,
f"{key}/alpha": self.alpha[key]})
else:
for key in self.model_keys:
info_train.update({f"{key}/alpha_loss": tf.Tensor(0.0, dtype=tf.float32),
f"{key}/alpha": self.alpha[key]})
return info_train
# @tf.function
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def learn(self, *inputs):
if self.distributed_training:
info_train = self.policy.mirrored_strategy.run(self.forward_fn, args=inputs)
return info_train[0]
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,
use_parameter_sharing=self.use_parameter_sharing,
use_actions_mask=False)
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']
IDs = sample_Tensor['agent_ids']
if self.use_parameter_sharing:
key = self.model_keys[0]
bs = batch_size * self.n_agents
rewards[key] = tf.reshape(rewards[key], [batch_size * self.n_agents])
terminals[key] = tf.reshape(terminals[key], [batch_size * self.n_agents])
else:
bs = batch_size
info = self.callback.on_update_start(self.iterations, method="update",
policy=self.policy, sample_Tensor=sample_Tensor, bs=bs)
info_train = self.learn(bs, obs, actions, rewards, obs_next, terminals, IDs, agent_mask)
for k, v in info_train.items():
info_train[k] = v.numpy()
info.update(info_train)
self.policy.soft_update(self.tau)
info.update(self.callback.on_update_end(self.iterations, method="update", policy=self.policy, info=info))
return info