This is from https://github.com/MoritzTaylor/ddpg-pytorch/blob/master/ddpg.py implementation and I guess most of the ddpg implementation are written this way.
self.critic_optimizer.zero_grad()
state_action_batch = self.critic(state_batch, action_batch)
value_loss = F.mse_loss(state_action_batch, expected_values.detach())
value_loss.backward()
self.critic_optimizer.step()
# Update the actor network
self.actor_optimizer.zero_grad()
policy_loss = -self.critic(state_batch, self.actor(state_batch))
policy_loss = policy_loss.mean()
policy_loss.backward()
self.actor_optimizer.step()
However after policy_loss.backwad(), I think the gradient is left in the critic network with respect to critic parameters. Shouldn't this affect the next update of critic?
If it does, what could be the solution?
