Python, Monte Carlo, Monte Carlo Simulation, Importance Sampling, Machine Learning.
calculate the total rewards based on the old policy instead and reweight them according to the new policy. The rewards from the old policy make up the proposal distribution. Here is the summary of when Importance Sampling is used: • p(x) is difficult to sample from. • We can evaluate p(x). • q(x)