def logprob(self, action_sample, pd_params):
means = pd_params[:, :, <NUM_LIT:0>]<EOL>log_std = pd_params[:, :, <NUM_LIT:1>]<EOL>std = torch.exp(log_std)<EOL>z_score = (action_sample - means) / std<EOL>return - (<NUM_LIT:0.5> * ((z_score**<NUM_LIT:2> + self.LOG2PI).sum(dim=-<NUM_LIT:1>)) + log_std.sum(dim=-<NUM_LIT:1>))<EOL>
Log-likelihood