Ultimately, the accumulated statistical data can also be fed back to train an even better neural network, leading to still more accurate Monte-Carlo data. Our approach is designed for a fairly tightly coupled parallel machine. Each node performs a Monte-Carlo simulation locally. Then, at frequent regular intervals, the nodes communicate to share results. The next Monte-Carlo simulation is then guided on the basis of these results.
All communications are either broadcasts from the master or global reductions.
We expect to soon have a player that is clearly better than the best humans. In our initial studies, we created Monte-Carlo players starting from three extremely simple neural network base players. Network no. 2 had the same weights and inputs as network no. These networks were highly optimized for speed, and are capable of making a move decision in about 0.2 msec on a single SP1 node. 3 uses the same raw board input as nos.