denisyarats/pytorch_sac

PyTorch implementation of Soft Actor-Critic (SAC)

pytorch_sac's Issues

Saved model weights

Hi, do you have saved model weights? That would be very helpful for me. Thanks!

Question about implementation details

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 😄

Continuous action space?

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?

Could you share the code you used to plot the results of your experiment?

data

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?

could you share the csv file of the benchmark data?

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?

Help to speed up the code

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!