Comments (5)
It looks like you might be looking at the RWR train step defined in training/diffusion.py. The DDPO update step is defined in training/policy_gradient.py:L63.
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Thanks, I am reproducing your work with GPU and almost finish it. I am one more question about your work.
What is the difference bettween policy-gradient verision of DDPO and GAN (maxmizing weighted likelihood)
Assume we have a fixed discriminator r(x_0), which is also treated as a reward model.
The obective function of the policy-gradient verision of DDPO and GAN should be the same, formulated as
E_x~p_\theta(x)[r(x_0)], the only difference is how to estimate or optimize this function, is that correct?
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There is indeed a connection between policy gradient algorithms and GANs, though it requires a little more machinery. I recommend checking out @caseychu's great paper on Probability Functional Descent for the full story.
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@jannerm Thanks!
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Related Issues (11)
- Log prob Computation HOT 2
- Question on the meaning of 'advantage' HOT 1
- Reward function on both aesthetic and prompt alignment HOT 1
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