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In GNU Backgammon the Rollout function implements the procedure described above, with the following improvements:

- Truncation: instead of rolling out all the way to the end of the game, it can stop and pretend its evaluation after a few plies is perfect. This may obviously introduce some amount of systematic error, but in practice this may not matter because:
- it makes rollouts much faster, which means you can do more of them (and thus trade sampling error for systematic error);
- different positions will be reached in different trials, so the correlation between errors in each trial weakens and the errors cancel out to some extent;
- if you are rolling out the positions after making different
plays, then any remaining systematic error between the two rollouts
is likely to be somewhat correlated and so the error in the
comparison between the plays is hopefully small. This implies that
truncated rollouts are better for estimating
*relative*equity (*which is the better move here, 13/10*/9 or 13/10* 6/5*?*) than*absolute*equity (*at this match score I need 29% wins to accept a dead cube; can I take in this position?*). - Race database truncation: when the game enters its 2-sided bearoff database, GNU Backgammon can estimate the probability of winning from that position with no error at all (it can play and evaluate endgame positions perfectly), which saves time and avoids introducing the errors that can result from large equity variances at the end of the game.
- Variance reduction: when using lookahead evaluations, it can
reduce errors by making use of the equity difference from one ply to
the next. (This can be interpreted as either canceling out the
estimated
*luck*(i.e. the difference in equity evaluations before and after rolling) or using subsequent evaluations to estimate the error in prior ones; the two views are equivalent). GNU Backgammon automatically performs variance reduction when looking ahead at least one ply. - Stratified sampling: uses quasi-random number generation instead of pseudo-random number generation (this is a standard technique in Monte Carlo simulations where having a near-perfect uniform distribution in your sample is more important than unpredictability). GNU Backgammon only stratifies the first 2 plies of a rollout, though it would be easy enough to extend it to the remainder.