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MishaLaskin
Optimising encoder twice during CURL?
Thanks for sharing your code, it's great to be able to go through the implementation.
Maybe I'm misunderstanding this, but it seem that if you intend self.cpc_optimizer to only optimise W, then
self.cpc_optimizer = torch.optim.Adam(
self.CURL.parameters(), lr=encoder_lr
)
should be
self.cpc_optimizer = torch.optim.Adam(
self.CURL.parameters(recursive=False), lr=encoder_lr
)
or
self.cpc_optimizer = torch.optim.Adam(
[self.CURL.W], lr=encoder_lr
)
The code I'm referring to is here and the torch docs for parameter are here. And I'm comparing it to section 4.7 of your paper.
As it stands it seems that encoder is optimised twice, once in encoder_optimizer and again in cpc_optimizer.
Or am I missing something?
Hi @wassname Thank you for pointing it out and I believe you are correct. Based on my very limited observation, optimizing the encoder once per call didn't affect the performance significantly in cheetah run. But it will be very helpful if someone could test it in more environments. Thanks.
Hi,
It seems that the encoder in actor never be updated either by loss or soft update (EMA), except that in the initialisation.
# tie encoders between actor and critic, and CURL and critic self.actor.encoder.copy_conv_weights_from(self.critic.encoder)
Only the encoder in critic/critic_target is updated by the critic_loss and the cpc.
Is there any insight for not updating the encoder in the actor?
Hi @IpadLi, I wondered on this a while back and mailed @MishaLaskin about it. This is the question I asked:
Why you are not updating the Shared encoder with the Actor Loss? Is there any specific reason for this?
@MishaLaskin 's reply:
I found that doing this resulted in more stable learning from pixels but it is also an empirical design choice and can be changed
Hi, can we integrate the update_critic function and update_cpc function by adding the critic_loss and cpc_loss together? Meanwhile, we only need two optimizers. Is it feasible?
self.cpc_optimizer = torch.optim.Adam([self.CURL.W], lr=encoder_lr) self.critic_optimizer = torch.optim.Adam(self.critic.parameters(), lr=critic_lr, betas=(critic_beta, 0.999)) loss = critic_loss + cpc_loss loss.backward() self.critic_optimizer.step() self.cpc_optimizer.step()
Hi,
It seems that the encoder in actor never be updated either by loss or soft update (EMA), except that in the initialisation.
# tie encoders between actor and critic, and CURL and critic self.actor.encoder.copy_conv_weights_from(self.critic.encoder)Only the encoder in critic/critic_target is updated by the critic_loss and the cpc.
Is there any insight for not updating the encoder in the actor?
The work of SAC+AE (https://arxiv.org/pdf/1910.01741.pdf) suggests to use the gradient from critic only (no actor) to update the encoder. Since this repo is based on the implementation of SAC+AE (as said in readme), I think CURL just follows it.
Hi @IpadLi, I wondered on this a while back and mailed @MishaLaskin about it. This is the question I asked:
Why you are not updating the Shared encoder with the Actor Loss? Is there any specific reason for this?
@MishaLaskin 's reply:
I found that doing this resulted in more stable learning from pixels but it is also an empirical design choice and can be changed
Hi, thanks for posting the reply from the author! Yet I don't think the reply answers the question -- even if we don't update the encoder with the actor loss, why shouldn't the actor encoder weights be copied from the critic encoder weights after each update to the critic encoder using the critic loss and cpc loss? It is a bit strange to me that two different encoders are used for the actor and the critic, where the paper seems to indicate there is only 1 shared encoder. Moreover, the weights of the actor encoder is never updated after initialization, so essentially only the MLP part of the actor is being trained/updated.
Update -- Sorry, the tie_weight function actually make the actor encoder and critic encoder share the same weights.
Hi @IpadLi, I wondered on this a while back and mailed @MishaLaskin about it. This is the question I asked:
Why you are not updating the Shared encoder with the Actor Loss? Is there any specific reason for this?
@MishaLaskin 's reply:
I found that doing this resulted in more stable learning from pixels but it is also an empirical design choice and can be changed
Hi, thanks for posting the reply from the author! Yet I don't think the reply answers the question -- even if we don't update the encoder with the actor loss, why shouldn't the actor encoder weights be copied from the critic encoder weights after each update to the critic encoder using the critic loss and cpc loss? It is a bit strange to me that two different encoders are used for the actor and the critic, where the paper seems to indicate there is only 1 shared encoder. Moreover, the weights of the actor encoder is never updated after initialization, so essentially only the MLP part of the actor is being trained/updated.
Update -- Sorry, the tie_weight function actually make the actor encoder and critic encoder share the same weights.
Hello! Does it mean the weights of actor encoder are still same with the critic encoder after the critic encoder is updated?
Hi @IpadLi, I wondered on this a while back and mailed @MishaLaskin about it. This is the question I asked:
Why you are not updating the Shared encoder with the Actor Loss? Is there any specific reason for this?
@MishaLaskin 's reply:
I found that doing this resulted in more stable learning from pixels but it is also an empirical design choice and can be changed
Hi, thanks for posting the reply from the author! Yet I don't think the reply answers the question -- even if we don't update the encoder with the actor loss, why shouldn't the actor encoder weights be copied from the critic encoder weights after each update to the critic encoder using the critic loss and cpc loss? It is a bit strange to me that two different encoders are used for the actor and the critic, where the paper seems to indicate there is only 1 shared encoder. Moreover, the weights of the actor encoder is never updated after initialization, so essentially only the MLP part of the actor is being trained/updated. Update -- Sorry, the tie_weight function actually make the actor encoder and critic encoder share the same weights.
Hello! Does it mean the weights of actor encoder are still same with the critic encoder after the critic encoder is updated?
yes,actor and critic indeed share the same encoder.
