[eref]

The main problem is that we still lack good generative models and good ways of interrogating them. GANs are unstable and difficult to apply to time series, VAEs suffer from posterior collapse, WaveNet/PixelRNN grow with the input size and overemphasize the details, RNNs are hard to train because we lack good training algorithms. Generally, small errors tend to compound in step-wise predictions because NNs do not generalize very well and gradients tend to vanish and shatter. If you just regard computation time to roll out the future, modeling domains in which the rules are simple enough to be hand-coded and evaluated quickly (such as Go and Chess) probably makes MCTS a million times more suitable compared to domains in which you need a complex model.

[gwern]

To expand on eref's comment a little: you absolutely could apply this or MCTS to ALE (and Guo et al 2014 did it very nicely). After all, the ALE is deterministic and simulatable by definition, so of course you can explore the game tree and reset the simulation as necessary. But people aren't much interested in this approach because using the ALE as a 'simulator' is cheating as far as testing full-strength AI techniques (we don't have simulators of the real world, after all), and the ALE games themselves (unlike Go) are of little intrinsic interest so there's no real benefit to engaging in cheating.