In compstat-lmu/rlR: Reinforcement Learning in R

library(rlR)
set.seed(123)
knitr::opts_chunk$set(cache = TRUE, collapse = FALSE, dev = "svg", fig.height = 3.5)
knitr::knit_hooks$set(document = function(x){
  gsub("```\n*```r*\n*", "", x)
})
library(reticulate)
os = import("os")
os$environ[["TF_CPP_MIN_LOG_LEVEL"]]="3"

rlR: repeated experiment

It make sense to repeatedly evaluate how an algorithm works for a particular scenario or environment. rlR provides the function rlR::repExperiment to serve this need. One could also use different cores to execute seperate experiment.

library(doMC)
registerDoMC(5)
# or
library(doParallel)
cl <- makeCluster(5)
registerDoParallel(cl)
res = repExperiment(sname = "CartPole-v0", aname = "AgentDQN", conf = getDefaultConf("AgentDQN"), nrep = 5, nepi = 5)

compstat-lmu/rlR documentation built on June 26, 2019, 5:56 p.m.