I am wondering if your idea of using GBDTs in combination with AlphaZero might not be most influential in areas where no neural network architecture is known to provide the right inductive bias for the problem at hand.
I think neural models are pretty unbeatable in many classic RL environments because convolutional neural networks are REALLY good at learning visual representations. In some sense, I suspect that the great success of AlphaGo Zero comes in big part from the fact that it really makes sense to analyze a Go board as a 2D image using convolutional networks: convolutional networks provide the right inductive bias for the problem of learning to play Go.
However, there are tasks where neural network are not as good, such as symbolic manipulation tasks (I am in a good position to know this as I'm doing research in the area of automated theorem proving). I would be very curious to see how your approach fares for those tasks.
I think neural models are pretty unbeatable in many classic RL environments because convolutional neural networks are REALLY good at learning visual representations. In some sense, I suspect that the great success of AlphaGo Zero comes in big part from the fact that it really makes sense to analyze a Go board as a 2D image using convolutional networks: convolutional networks provide the right inductive bias for the problem of learning to play Go.
However, there are tasks where neural network are not as good, such as symbolic manipulation tasks (I am in a good position to know this as I'm doing research in the area of automated theorem proving). I would be very curious to see how your approach fares for those tasks.