"""
Policy Gradient (PG)
Paper link: https://proceedings.neurips.cc/paper/2001/file/4b86abe48d358ecf194c56c69108433e-Paper.pdf
Implementation: MindSpore
"""
from argparse import Namespace
from mindspore import nn
from xuance.mindspore import ms, msd, ops, Module, Tensor, optim
from xuance.mindspore.learners import Learner
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class PG_Learner(Learner):
def __init__(self,
config: Namespace,
policy: Module,
callback):
super(PG_Learner, self).__init__(config, policy, callback)
self.optimizer = optim.Adam(params=self.policy.trainable_params(), lr=self.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.ent_coef = config.ent_coef
self.mse_loss = nn.MSELoss()
self.softmax = nn.Softmax(axis=-1)
self.is_continuous = self.policy.is_continuous
self.a_dist = msd.Normal(dtype=ms.float32) if self.is_continuous else msd.Categorical()
# 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_batch, act_batch, ret_batch):
if self.is_continuous:
outputs, mu, std, v_pred = self.policy(obs_batch)
log_prob = self.a_dist._log_prob(value=act_batch, mean=mu, sd=std)
log_prob = ops.reduce_sum(x=log_prob, axis=-1)
entropy = self.a_dist._entropy(mean=mu, sd=std)
entropy = ops.reduce_sum(x=entropy, axis=-1)
else:
outputs, logits, v_pred = self.policy(obs_batch)
probs = self.softmax(logits)
log_prob = self.a_dist._log_prob(value=act_batch, probs=probs)
entropy = self.a_dist.entropy(probs=probs)
a_loss = -ops.mean(ret_batch * log_prob)
e_loss = ops.mean(entropy)
loss = a_loss - self.ent_coef * e_loss
return loss, a_loss, e_loss, outputs, log_prob
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def update(self, **samples):
self.iterations += 1
obs_batch = Tensor(samples['obs'])
act_batch = Tensor(samples['actions'])
ret_batch = Tensor(samples['returns'])
info = self.callback.on_update_start(self.iterations,
policy=self.policy, obs=obs_batch, act=act_batch, returns=ret_batch)
(loss, a_loss, e_loss, outputs, log_prob), grads = self.grad_fn(obs_batch, act_batch, ret_batch)
self.optimizer(grads)
self.scheduler.step()
lr = self.scheduler.get_last_lr()[0]
info.update({
"total-loss": loss.asnumpy(),
"actor-loss": a_loss.asnumpy(),
"entropy": e_loss.asnumpy(),
"learning_rate": lr.asnumpy(),
})
info.update(self.callback.on_update_end(self.iterations,
policy=self.policy, info=info, rep_output=outputs,
log_prob=log_prob, a_loss=a_loss, e_loss=e_loss, loss=loss))
return info