Yes, I have read this tutorial before. But I use recurrent PG in task-oriented spoken dialog system. If the behavior of user is much easy, our algorithm is easy to converge. If the behavior of user is more complicated, it seems our algorithm has difficult in learning.
The first figure shows the success rate of simple user, the second and third show the success rate of complicated user. It’s obvious that the same algorithm will be slower to converge in more difficult environment.
What I mean is if there exist some variants of DRL to make sure RL converge faster in complicated environment. And how can I make sure my DRL will do better after one update. In my experiment, I found I can not make sure my RL will get more success rate after one update.