Combining deep reinforcement learning with alpha-beta pruning I will explain my question in relation to chess, but it should be relevant for other games as well:

In short terms: Is it possible to combine the techniques used by AlphaZero with those used by, say, Stockfish? And if so, has it been attempted?

I have only a brief knowledge about how AlphaZero works, but from what I've understood, it basically takes the board state as input to a neural net, possibly combined with monte carlo methods, and outputs a board evaluation or prefered move. To me, this really resembles the heuristic function used by traditional chess engines like stockfish.

So, from this I will conclude (correct me if I'm wrong) that AlphaZero evaluates the current position, but uses a very powerful heuristic. Stockfish on the other hand searches through lots of positions from the current one first, and then uses a less powerful heuristic when a certain depth is reached.

Is it therefore possible to combine these approaches by first using alpha-beta pruning, and then using AlphaZero as some kind of heuristic when the max depth is reached? To me it seems like this would be better than just evaluating the current position like (I think) AlphaZero does. Will it take too much time to evaluate? Or is it something I have misunderstood? If it's possible, has anyone attempted it?
Yes it's possible to to combine AlphaZero with Minimax methods (including alpha-beta pruning). AlphaZero itself is combination of  Monte Carlo Tree Search (MCTS) and Deep Network, where MCTS is used to get data to train network and network used for tree leafs evaluation (instead of rollout as in classical MCTS). It's possible to combine selection-expansion part of AlphaZero MCTS with Minimax the same way as it was done for classical MCTS - "Monte-Carlo Tree Search and Minimax Hybrids", pdf.