"""
QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning
Paper link:
http://proceedings.mlr.press/v97/son19a/son19a.pdf
Implementation: TensorFlow 2.X
"""
from argparse import Namespace
from operator import itemgetter
from xuance.common import List
from xuance.tensorflow import tf, tk, Module
from xuance.tensorflow.learners import LearnerMAS
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class QTRAN_Learner(LearnerMAS):
def __init__(self,
config: Namespace,
model_keys: List[str],
agent_keys: List[str],
policy: Module,
callback):
super(QTRAN_Learner, self).__init__(config, model_keys, agent_keys, policy, callback)
self.build_optimizer()
self.n_actions = {k: self.policy.action_space[k].n for k in self.model_keys}
self.sync_frequency = config.sync_frequency
self.mse_loss = tk.losses.MeanSquaredError()
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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 = tk.optimizers.legacy.Adam(self.config.learning_rate)
else:
self.optimizer = tk.optimizers.Adam(self.config.learning_rate)
# @tf.function
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def forward_fn(self, bs, batch_size, state, obs, actions, rewards_tot, state_next, obs_next, terminals_tot,
agent_mask, avail_actions, avail_actions_next, IDs):
with tf.GradientTape() as tape:
_, hidden_state, actions_greedy, q_eval = self.policy(obs, agent_ids=IDs, avail_actions=avail_actions)
_, hidden_state_next, q_next = self.policy.Qtarget(obs_next, agent_ids=IDs)
q_eval_a, q_eval_greedy_a, q_next_a = {}, {}, {}
actions_next_greedy = {}
for key in self.model_keys:
mask_values = agent_mask[key]
q_eval_a[key] = tf.reshape(tf.gather(q_eval[key], tf.cast(actions[key][:, None], dtype=tf.int32),
axis=-1, batch_dims=-1), [bs])
q_eval_greedy_a[key] = tf.reshape(tf.gather(q_eval[key], tf.cast(actions_greedy[key][:, None],
dtype=tf.int32),
axis=-1, batch_dims=-1), [bs])
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)
actions_next_greedy[key] = act_next[key]
q_next_a[key] = tf.reshape(tf.gather(q_next[key], act_next[key][:, None],
axis=-1, batch_dims=-1), [bs])
else:
actions_next_greedy[key] = q_next[key].argmax(dim=-1, keepdim=False)
q_next_a[key] = q_next[key].max(dim=-1, keepdim=True).values.reshape(bs)
q_eval_a[key] *= mask_values
q_eval_greedy_a[key] *= mask_values
q_next_a[key] *= mask_values
if self.config.agent == "QTRAN_base":
# -- TD Loss --
q_joint, v_joint = self.policy.Q_tran(state, hidden_state, actions, agent_mask)
q_joint_next, _ = self.policy.Q_tran_target(state_next, hidden_state_next, actions_next_greedy,
agent_mask)
y_dqn = rewards_tot + (1 - terminals_tot) * self.gamma * q_joint_next
loss_td = self.mse_loss(tf.stop_gradient(y_dqn), q_joint) # TD loss
# -- Opt Loss --
# Argmax across the current agents' actions
q_tot_greedy = self.policy.Q_tot(q_eval_greedy_a)
q_joint_greedy_hat, _ = self.policy.Q_tran(state, hidden_state, actions_greedy, agent_mask)
error_opt = q_tot_greedy - tf.stop_gradient(q_joint_greedy_hat) + v_joint
loss_opt = tf.reduce_mean(error_opt ** 2) # Opt loss
# -- Nopt Loss --
q_tot = self.policy.Q_tot(q_eval_a)
q_joint_hat = q_joint
error_nopt = q_tot - tf.stop_gradient(q_joint_hat) + v_joint
error_nopt = tf.clip_by_value(error_nopt, clip_value_min=-1e10, clip_value_max=0)
loss_nopt = tf.reduce_mean(error_nopt ** 2) # NOPT loss
q_joint_mean = tf.reduce_mean(q_joint)
elif self.config.agent == "QTRAN_alt":
# -- TD Loss -- (Computed for all agents)
q_count, v_joint = self.policy.Q_tran(state, hidden_state, actions, agent_mask)
actions_choosen = itemgetter(*self.model_keys)(actions)
actions_choosen = tf.reshape(actions_choosen, [-1, self.n_agents, 1])
q_joint_choosen = tf.reshape(tf.gather(q_count, tf.cast(actions_choosen, dtype=tf.int32),
axis=-1, batch_dims=-1), [-1, self.n_agents])
q_next_count, _ = self.policy.Q_tran_target(state_next, hidden_state_next, actions_next_greedy,
agent_mask)
actions_next_choosen = itemgetter(*self.model_keys)(actions_next_greedy)
actions_next_choosen = tf.reshape(actions_next_choosen, [-1, self.n_agents, 1])
q_joint_next_choosen = tf.reshape(tf.gather(q_next_count, tf.cast(actions_next_choosen, dtype=tf.int32),
axis=-1, batch_dims=-1), [-1, self.n_agents])
y_dqn = rewards_tot + (1 - terminals_tot) * self.gamma * q_joint_next_choosen
loss_td = self.mse_loss(tf.stop_gradient(y_dqn), q_joint_choosen) # TD loss
# -- Opt Loss -- (Computed for all agents)
q_tot_greedy = self.policy.Q_tot(q_eval_greedy_a)
q_joint_greedy_hat, _ = self.policy.Q_tran(state, hidden_state, actions_greedy, agent_mask)
actions_greedy_current = itemgetter(*self.model_keys)(actions_greedy)
actions_greedy_current = tf.reshape(actions_greedy_current, [-1, self.n_agents, 1])
q_joint_greedy_hat_all = tf.reshape(tf.gather(q_joint_greedy_hat, tf.cast(actions_greedy_current,
dtype=tf.int32),
axis=-1, batch_dims=-1), [-1, self.n_agents])
error_opt = q_tot_greedy - tf.stop_gradient(q_joint_greedy_hat_all) + v_joint
loss_opt = tf.reduce_mean(error_opt ** 2) # Opt loss
# -- Nopt Loss --
q_eval_count = tf.reshape(itemgetter(*self.model_keys)(q_eval), [batch_size * self.n_agents, -1])
q_sums = tf.reshape(itemgetter(*self.model_keys)(q_eval_a), [-1, self.n_agents])
q_sums_repeat = tf.tile(q_sums[:, None], [1, self.n_agents, 1])
agent_mask_diag = tf.tile((1 - tf.eye(self.n_agents, dtype=tf.float32))[None], [batch_size, 1, 1])
q_sum_mask = tf.reduce_sum(q_sums_repeat * agent_mask_diag, axis=-1)
q_count_for_nopt = tf.reshape(q_count, [batch_size * self.n_agents, -1])
v_joint_repeated = tf.reshape(tf.tile(v_joint, [1, self.n_agents]), [-1, 1])
error_nopt = q_eval_count + tf.reshape(q_sum_mask, [-1, 1]) - tf.stop_gradient(q_count_for_nopt) + v_joint_repeated
error_nopt_min = tf.reduce_min(error_nopt, axis=-1)
loss_nopt = tf.reduce_mean(error_nopt_min ** 2) # NOPT loss
q_joint_mean = tf.reduce_mean(q_joint_choosen)
else:
raise ValueError("Mixer {} not recognised.".format(self.config.agent))
# calculate the loss function
loss = loss_td + self.config.lambda_opt * loss_opt + self.config.lambda_nopt * loss_nopt
gradients = tape.gradient(loss, self.policy.parameters_model)
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.parameters_model))
else:
self.optimizer.apply_gradients(zip(gradients, self.policy.parameters_model))
return q_joint_mean, loss_td, loss_opt, loss_nopt, loss
# @tf.function
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def learn(self, *inputs):
if self.distributed_training:
q_joint_mean, loss_td, loss_opt, loss_nopt, loss = self.policy.mirrored_strategy.run(self.forward_fn, args=inputs)
return (self.policy.mirrored_strategy.reduce(tf.distribute.ReduceOp.SUM, q_joint_mean, axis=None),
self.policy.mirrored_strategy.reduce(tf.distribute.ReduceOp.SUM, loss_td, axis=None),
self.policy.mirrored_strategy.reduce(tf.distribute.ReduceOp.SUM, loss_opt, axis=None),
self.policy.mirrored_strategy.reduce(tf.distribute.ReduceOp.SUM, loss_nopt, axis=None),
self.policy.mirrored_strategy.reduce(tf.distribute.ReduceOp.SUM, loss, 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=True)
batch_size = sample_Tensor['batch_size']
state = sample_Tensor['state']
state_next = sample_Tensor['state_next']
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 = tf.reshape(tf.reduce_mean(rewards[key], axis=1), [batch_size, 1])
terminals_tot = tf.reshape(tf.reduce_prod(terminals[key], axis=1), [batch_size, 1])
else:
bs = batch_size
rewards_tot = tf.reduce_mean(tf.stack(itemgetter(*self.agent_keys)(rewards), axis=1),
axis=-1, keepdims=True)
terminals_tot = tf.reduce_prod(tf.stack(itemgetter(*self.agent_keys)(terminals), axis=1),
axis=1, keepdims=True)
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_joint_mean, loss_td, loss_opt, loss_nopt, loss = self.learn(bs, batch_size, state, obs, actions, rewards_tot,
state_next, obs_next, terminals_tot, agent_mask,
avail_actions, avail_actions_next, IDs)
info.update({
"Q_joint": q_joint_mean.numpy(),
"loss_td": loss_td.numpy(),
"loss_opt": loss_opt.numpy(),
"loss_nopt": loss_nopt.numpy(),
"loss": loss.numpy()
})
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_joint_mean=q_joint_mean))
return info