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View Code? Open in Web Editor NEWPyTorch implementation of Soft Actor-Critic (SAC)
License: MIT License
PyTorch implementation of Soft Actor-Critic (SAC)
License: MIT License
Hi, do you have saved model weights? That would be very helpful for me. Thanks!
Hey,
First, I want to thank you for your implementation of SAC which is of high quality.
I want to ask you about two details of implementation which I don't understand well.
First, why here instead of clamping the log_std
you use this constrain here here? How do you choose 2
, -5
?
# constrain log_std inside [log_std_min, log_std_max]
log_std = torch.tanh(log_std)
log_std_min, log_std_max = self.log_std_bounds
log_std = log_std_min + 0.5 * (log_std_max - log_std_min) * (log_std + 1)
Second, why you approximate atanh
with 0.5 * (x.log1p() - (-x).log1p())
here instead of torch.atanh()
?
Thank you for your responses ๐
Hi, thanks for sharing!
I would like to ask a question, is the output of the SAC algorithm you implemented a continuous action space?
Could you share the code you used to plot the results of your experiment?
Hello, author. I was inspired by the code of your project. I want to ask you for leave. I don't quite understand the meaning of the parameters in the code data. Can you explain it?
Thank you for sharing excellent work!
The cost of state-based training is also expensive but necessary. Could you share the state-based benchmark data?
Hi, the code currently takes around 15hours for a million epochs, could you guide me into increasing this speed and not deteriorating the performance. As the compute required for the code is pretty less, i would like to utilize them to speed up. Thanks!
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