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@Ankit: any error estimates?
@Ankit: isn’t the action space infinite for your reinforcement learning? How do you handle that?
Hi Wasiur…We don’t have any rigorous error estimates but we can plot the loss function trajectories for the validation data to see if the neural network model has been trained successfully.
The action space is a mapping emulated by a neural network. So we don’t need to explicitly handle the infiniteness of the states-space.
Thank you for the interesting talk, Greg. You mentioned that S and I in the SIR model are trackable. Could you explain why the species R is not trackable? Intuitively, it appears that R can be 'tracked' (actually every single individual in a system) as there is no birth event in the system.
I meant to say that SIR are all trackable
But R are trackable trivially
I see, thank you for the answer!
B/c R are not changing state
@Jinsoo. Can you apply your approach to a rare event , e.g. extinction? In that case, the system operates with two time constants, one for convergence to quasi-stationary distribution and another for leakage to extinction.
@wasiur: very nice talk, I will get your paper off the arXiv and email you with questions (had to leave before end). Thanks!
Jae Kyoung Kim
Thanks Wasiur for the great talk. Could you explain FPT approximation formula one more time….if time is allowed…..?