Set.seed in monte Carlo

#year 2022 R.100<-runif(n=100, min = 40, max = 65) R.100 summary(R.100) R.norm.100<-rnorm(length(R.100), mean = 50, sd=7) R.norm.100 summary(R.norm.100) a=0.6 C=100 f<-seq(from=1.10, to=1.18, by=0.005) B<-NULL B<-as.data.frame(B) for(i in 1:length(f)) { for(R in 1:length(R.norm.100)) { B[i, “degerler”] <- (R.norm.100[R]-(1-a)*C*f[i]) / R.norm.100[R] } } summary(B) When running the above loop function and calculating B, I want that the…

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Can we use imitation learning for on-policy algorithms?

Imitation learning uses experiences of an (expert) agent to train another agent, in my understanding. If I want to use an on-policy algorithm, for example, Proximal Policy Optimization, because of it’s on-policy nature we cannot use the experiences generated by another policy directly. Importance Sampling can be used to overcome this limitation, however, it is…

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