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It would be very interesting to see proper MCTS applied to 2048. Monte Carlo Tree Search performs well on problems with linear reward. 2048 has exponential reward, with subsequent states having much higher tile scores. This nonlinearity tends to cause MCTS to fall into traps - one rollout will find a state evaluation with a much higher score, and will thereafter bias all subsequent rollouts to that same (potentially suboptimal) path. If there are clever ways around this, such as board evaluation heuristics that scale linearly, then I'd love to see them. In practice, a minimax approach that enumerates all successor states up to a particular depth has worked best for me.



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