Question about using Importance Sampling in BEAR

[wadx2019]

Hello, I have some problem with the BEAR_IS in your algos.py file.

As is known to us, DDPG is actually one-step Q-learning in continuous tasks and BEAR also takes such architechture. Now that it makes no sense to use importance sampling in BEAR because the difference between current policy and behavioral policy doesn't result in the inaccuracy of the estimation of Q-value.

So Can you explain why you wrote a importance sampling version of BEAR in your project?