Simulators should be fast, sure, however that they should be deterministic to be fast is something I do not believe. We have millions of programs already that make use of pseudo random generated numbers, and they don't seem to be suffering performance problems because of that.
And about the state and memory concern, mcts does not care directly about it, since for it the simulator is used as a black box and its internals are irrelevant to it. Instead, as long as any environment configuration can be described in terms of state (essentially a unique state->number conversion must be possible - and even then not always) mcts will work. And since it also does not care about the size of the state space, the concern that having memory as one of the factors in the state would be problematic is also unfounded.
I also disagree on the specificity of AlphaGO. Mcts has been used successfully in many fields after its initial usage and tuning for Go. I did my thesis on similar algorithms. In the same way, it does not matter whether AlphaGO can be directly used on other problems. What matters is the new idea of using NNs in order to improve substantially and with little overhead the value estimations used by mcts to explore the decision tree. This is the true breakthrough. The fact that the first implementation of this idea is a Go playing program is irrelevant, it's more like a showcase of the goodness of the approach.
And about the state and memory concern, mcts does not care directly about it, since for it the simulator is used as a black box and its internals are irrelevant to it. Instead, as long as any environment configuration can be described in terms of state (essentially a unique state->number conversion must be possible - and even then not always) mcts will work. And since it also does not care about the size of the state space, the concern that having memory as one of the factors in the state would be problematic is also unfounded.
I also disagree on the specificity of AlphaGO. Mcts has been used successfully in many fields after its initial usage and tuning for Go. I did my thesis on similar algorithms. In the same way, it does not matter whether AlphaGO can be directly used on other problems. What matters is the new idea of using NNs in order to improve substantially and with little overhead the value estimations used by mcts to explore the decision tree. This is the true breakthrough. The fact that the first implementation of this idea is a Go playing program is irrelevant, it's more like a showcase of the goodness of the approach.