es, this noise will eventually give each move an initial boost. However, since training effectively aggregates billions of positions, moves have to prove themselves worth the boost, since they are equivalently dragged down some of the time instead. This serves the desired uncertainty and mean reversion of (b) above. Because of the relatively small simulation budget, aggregation across positions, and tactical volatility between similar-looking positions, the network tends to absorb universal truths rather th