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In the example for RNaD, the importance sampling correction for get_loss_nerd is 1. This is because the example provided is the on-policy case, and there are synchronous updates of the policy between acting and learning.
My question is what needs to be changed for this example to be used in an asynchronous off-policy setting? Is it as simple as substituting the importance sampling correction for a policy ratio term? What would this look like exactly?
How could I construct the importance sampling correction for the off-policy case?
The text was updated successfully, but these errors were encountered:
In the example for RNaD, the importance sampling correction for get_loss_nerd is 1. This is because the example provided is the on-policy case, and there are synchronous updates of the policy between acting and learning.
My question is what needs to be changed for this example to be used in an asynchronous off-policy setting? Is it as simple as substituting the importance sampling correction for a policy ratio term? What would this look like exactly?
How could I construct the importance sampling correction for the off-policy case?
The text was updated successfully, but these errors were encountered: