"""
Deep Deterministic Policy Gradient (DDPG)
Paper link: https://arxiv.org/pdf/1509.02971.pdf
Implementation: MindSpore
"""
from argparse import Namespace
from mindspore import nn
from xuance.mindspore import ms, ops, Module, Tensor, optim
from xuance.mindspore.learners import Learner
from xuance.mindspore.utils import clip_grads
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class DDPG_Learner(Learner):
def __init__(self,
config: Namespace,
policy: Module,
callback):
super(DDPG_Learner, self).__init__(config, policy, callback)
self.optimizer = {
'actor': optim.Adam(params=self.policy.actor_parameters, lr=self.config.learning_rate, eps=1e-5),
'critic': optim.Adam(params=self.policy.critic_parameters, lr=self.config.learning_rate, eps=1e-5),
}
self.scheduler = {
'actor': optim.lr_scheduler.LinearLR(self.optimizer['actor'], start_factor=1.0, end_factor=self.end_factor_lr_decay,
total_iters=self.config.running_steps),
'critic': optim.lr_scheduler.LinearLR(self.optimizer['critic'], start_factor=1.0, end_factor=self.end_factor_lr_decay,
total_iters=self.config.running_steps)
}
self.tau = config.tau
self.gamma = config.gamma
self.mse_loss = nn.MSELoss()
# Get gradient function
self.grad_fn_actor = ms.value_and_grad(self.forward_fn_actor, None, self.optimizer['actor'].parameters,
has_aux=True)
self.grad_fn_critic = ms.value_and_grad(self.forward_fn_critic, None, self.optimizer['critic'].parameters,
has_aux=True)
self.policy.set_train()
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def forward_fn_actor(self, obs_batch):
policy_q = self.policy.Qpolicy(obs_batch)
p_loss = -ops.mean(policy_q)
return p_loss, policy_q
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def forward_fn_critic(self, obs_batch, act_batch, next_batch, rew_batch, ter_batch):
action_q = self.policy.Qaction(obs_batch, act_batch).reshape([-1])
next_q = self.policy.Qtarget(next_batch).reshape([-1])
target_q = rew_batch + (1 - ter_batch) * self.gamma * next_q
q_loss = self.mse_loss(logits=action_q, labels=ops.stop_gradient(target_q))
return q_loss, action_q, next_q, target_q
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def update(self, **samples):
self.iterations += 1
obs_batch = Tensor(samples['obs'], dtype=ms.float32)
act_batch = Tensor(samples['actions'], dtype=ms.float32)
rew_batch = Tensor(samples['rewards'], dtype=ms.float32)
next_batch = Tensor(samples['obs_next'], dtype=ms.float32)
ter_batch = Tensor(samples['terminals'], dtype=ms.float32)
info = self.callback.on_update_start(self.iterations,
policy=self.policy, obs=obs_batch, act=act_batch,
next_obs=next_batch, rew=rew_batch, termination=ter_batch)
(q_loss, action_q, next_q, target_q), grads_critic = self.grad_fn_critic(
obs_batch, act_batch, next_batch, rew_batch, ter_batch)
if self.use_grad_clip:
grads_critic = clip_grads(grads_critic, Tensor(-self.grad_clip_norm), Tensor(self.grad_clip_norm))
self.optimizer['critic'](grads_critic)
(p_loss, policy_q), grads_actor = self.grad_fn_actor(obs_batch)
if self.use_grad_clip:
grads_actor = clip_grads(grads_actor, Tensor(-self.grad_clip_norm), Tensor(self.grad_clip_norm))
self.optimizer['actor'](grads_actor)
self.policy.soft_update(self.tau)
self.scheduler['actor'].step()
self.scheduler['critic'].step()
actor_lr = self.scheduler['actor'].get_last_lr()[0]
critic_lr = self.scheduler['critic'].get_last_lr()[0]
info.update({
"Qloss": q_loss.asnumpy(),
"Ploss": p_loss.asnumpy(),
"Qvalue": action_q.mean().asnumpy(),
"actor_lr": actor_lr.asnumpy(),
"critic_lr": critic_lr.asnumpy()
})
info.update(self.callback.on_update_end(self.iterations,
policy=self.policy, info=info,
action_q=action_q, next_q=next_q, target_q=target_q, policy_q=policy_q,
q_loss=q_loss, p_loss=p_loss))
return info