Following Le Monde puzzle #810, I tried to code an R program (not reproduced here) to optimise an awalé game but the recursion was too rich for R:

Error: evaluation nested too deeply:
infinite recursion / options(expressions=)?

even with a very small number of holes and seeds in the awalé… Searching on the internet, it seems the computer simulation of a winning strategy for an awalé game still is an open problem! Here is a one-step R function that does  not produce sure gains for the first player, far from it, as shown by the histogram below…  I would need a less myopic strategy by iterating  this function at least twice.

onemorestep=function(x,side){
# x current state of the awale,
# side side of the awale (0 vs 1)

M=length(x);N=as.integer(M/2)
rewa=rep(0,M)
newb=matrix(0,ncol=M,nrow=M)

for (i in ((1:N)+N*side)){

 if (x[i]>0){
   y=x
   y[i]=0
   for (t in 0:(x[i]-1))
     y[1+(i+t)%%M]=y[1+(i+t)%%M]+1

   last=1+(i+t)%%M
   if (side){ gain=(last<=N)
    }else{ gain=(last>N)}

   if (gain){# ending up on the right side
     rewa[i]=0
     while (((last>0)&&(side))||((last>N)||(!side)))
     if ((y[last]==2)||(y[last]==3)){
          rewa[i]=rewa[i]+y[last];y[last]=0
          last=last-1
          }else{ break()}
     }
   newb[i,]=y
   }
  }
if (max(rewa)>0){
  sol=order(-rewa)[1]
  }else{ sol=rang=((1:N)+N*side)[x[((1:N)+N*side)]>0]
   if (length(rang)>1) sol=sample(rang,1,prob=x[rang]^3)}

   return(list(reward=max(rewa),board=newb[sol,]))
}