"""
Independent Q-learning (IQL)
Implementation: MindSpore
"""
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
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class IQL_Learner(LearnerMAS):
def __init__(self,
config: Namespace,
model_keys: List[str],
agent_keys: List[str],
policy: Module,
callback):
super(IQL_Learner, self).__init__(config, model_keys, agent_keys, policy, callback)
self.optimizer = {key: optim.Adam(params=self.policy.parameters_model[key], lr=self.config.learning_rate,
eps=1e-5) for key in self.model_keys}
self.scheduler = {key: optim.lr_scheduler.LinearLR(self.optimizer[key], start_factor=1.0, end_factor=self.end_factor_lr_decay,
total_iters=self.config.running_steps)
for key in self.model_keys}
self.gamma = config.gamma
self.sync_frequency = config.sync_frequency
self.n_actions = {k: self.policy.action_space[k].n for k in self.model_keys}
# Get gradient function
self.grad_fn = {key: ms.value_and_grad(self.forward_fn, None, self.optimizer[key].parameters, has_aux=True)
for key in self.model_keys}
self.policy.set_train()
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def forward_fn(self, obs, actions, agt_mask, avail_actions, ids, q_target, agent_key):
rnn_hidden = None
_, _, q_eval = self.policy(observation=obs, agent_ids=ids, avail_actions=avail_actions,
agent_key=agent_key, rnn_hidden=rnn_hidden)
q_eval_a = q_eval[agent_key].gather(actions[agent_key].astype(ms.int32).unsqueeze(-1), axis=-1, batch_dims=-1)
td_error = (q_eval_a.reshape(-1) - q_target) * agt_mask
loss = (td_error ** 2).sum() / agt_mask.sum()
return loss, q_eval_a, td_error
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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[key] = rewards[key].reshape(batch_size * self.n_agents)
terminals[key] = terminals[key].reshape(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)
_, q_next = self.policy.Qtarget(observation=obs_next, agent_ids=IDs)
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:
_, actions_next_greedy, _ = self.policy(obs_next, IDs, agent_key=key, avail_actions=avail_actions)
q_next_a = q_next[key].gather(actions_next_greedy[key].unsqueeze(-1).long(), -1, -1).reshape(bs)
else:
q_next_a = q_next[key].max(dim=-1, keepdim=True).values.reshape(bs)
q_target = rewards[key] + (1 - terminals[key]) * self.gamma * q_next_a
(loss, q_eval_a, td_error), grads = self.grad_fn[key](obs, actions, mask_values,
avail_actions, IDs, q_target, key)
if self.use_grad_clip:
grads = clip_grads(grads, Tensor(-self.grad_clip_norm), Tensor(self.grad_clip_norm))
self.optimizer[key](grads)
self.scheduler[key].step()
lr = self.scheduler[key].get_last_lr()[0]
info.update({
f"{key}/learning_rate": lr.asnumpy(),
f"{key}/loss_Q": loss.asnumpy(),
f"{key}/predictQ": q_eval_a.mean().asnumpy()
})
info.update(self.callback.on_update_agent_wise(self.iterations, key, info=info, method="update",
mask_values=mask_values, q_eval_a=q_eval_a,
q_next_a=q_next_a, q_target=q_target,
td_error=td_error, loss=loss))
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))
return info