I am not really satisfied with the available analysis of why AlphaZero uses MCTS instead of Alpha Beta search. Some analysis claim that its because MCTS is a lot more humanlike. I disagree because I don't think AlphaZero was really concerned about becoming humanlike. The fact that it ended up playing like humans was just a coincidence but it was never the goal behind its design choice.
In the AlphaZero paper,
MCTS and Alpha-Beta Search
For at least four decades the strongest computer chess programs have used alpha-beta search (18, 23). AlphaZero uses a markedly different approach that averages over the position evaluations within a subtree, rather than computing the minimax evaluation of that subtree. However, chess programs using traditional MCTS were much weaker than alpha-beta search programs, (4, 24); while alpha-beta programs based on neural networks have previously been unable to compete with faster, handcrafted evaluation functions. AlphaZero evaluates positions using non-linear function approximation based on a deep neural network, rather than the linear function approximation used in typical chess programs. This provides a much more powerful representation, but may also introduce spurious approximation errors. MCTS averages over these approximation errors, which therefore tend to cancel out when evaluating a large subtree. In contrast, alpha-beta search computes an explicit minimax, which propagates the biggest approximation errors to the root of the subtree. Using MCTS may allow AlphaZero to effectively combine its neural network representations with a powerful, domain-independent search.
What do they mean by
approach that averages over the position evaluations within a subtree, rather than computing the minimax evaluation of that subtree.
What does it mean to average over position evaluations within a subtree?
Also in the next part,
AlphaZero evaluates positions using non-linear function approximation based on a deep neural network, rather than the linear function approximation used in typical chess programs.
How is alphazero's evaluation non linear and how are the typical programs linear?
Could someone dumb these down?
My own guess behind MCTS over AB is because MCTS returns visit counts for each of the moves, and this data can be used to train the Policy Network in Alphazero. A minimax(AB) would return just that one best move, which could be used to train value network, but it cannot be used to train Policy network. So MCTS exists to train policy network in Alphazero. Is this a good or a bad guess?
Also, please do not mark these as multiple questions, I believe all these are a part of the same question.