Deep learning in Monte Carlo Tree Search
Even with MCTS-UCT, computers could still not even come close to beating the best Go players; however, in 2016, a team from Google Deep Mind developed an AI they called AlphaGo. It defeated Lee Sedol, the world's top Go player, over a five game series, winning 4-1. The way they did this was using three improvements over the standard MCTS UCT approach.
If we were to think about why MCTS is so inaccurate, an intuitive answer that might arise is that the moves used in the evaluation are selected randomly when we know that some moves are much more likelier than others. In Go, when there is a battle for control of a corner, the moves around that area are much better candidates for selection, as opposed to moves on the opposite side of the board. If we had a good way of selecting which moves are likely to be played, we would have massively reduced the breadth of our search, and by extension, increased the accuracy of our MCTS evaluations. If we go back to...
