POMDPs is exactly how one formalizes imperfect information games. This is where the concept of information sets comes from. To answer your question, any two player algorithm is going to apply to single player games as it is trivial to transform. For games like 2048, the "adversary" is simply the opposite of your outcome. For games where you are trying to maximize your score, this is the standard RL setting and any of the Atari algorithms (including MuZero) can be used.
POMDPs? Are those considered to be much simpler problems?
Well, solving a Partially Observed Markov Decision Processes in general isn't just NP-complete but actually undecidable. So I'm not sure how one measures SOTA (state of the art).
It strongly depends on what type of structure you can assume and how expensive sampling is. Dreamerv2, agent57 on Atari, dreamerv2 and the generalized agent model trained on 600 tasks by deepmind might be worth looking into for different approaches on pomdps, but you can do much better if you impose physics priors by e.g. using neural ODEs for the latent state modeling.
POMDP just means "observations are not state" and that you need to use a stateful policy to infer the state somehow, but without further assumptions it's difficult to answer this question
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What is the SOTA on solving non-adversarial (single player?) POMDPs? Are those considered to be much simpler problems?
POMDPs is exactly how one formalizes imperfect information games. This is where the concept of information sets comes from. To answer your question, any two player algorithm is going to apply to single player games as it is trivial to transform. For games like 2048, the "adversary" is simply the opposite of your outcome. For games where you are trying to maximize your score, this is the standard RL setting and any of the Atari algorithms (including MuZero) can be used.
In case you are wondering about cooperative multi-agent games, I would check this group's publications: https://www.cs.ox.ac.uk/people/publications/date/Shimon.Whit...
POMDPs? Are those considered to be much simpler problems?
Well, solving a Partially Observed Markov Decision Processes in general isn't just NP-complete but actually undecidable. So I'm not sure how one measures SOTA (state of the art).
You measure it by practical performance on a test suite of important POMDPs and games.
It strongly depends on what type of structure you can assume and how expensive sampling is. Dreamerv2, agent57 on Atari, dreamerv2 and the generalized agent model trained on 600 tasks by deepmind might be worth looking into for different approaches on pomdps, but you can do much better if you impose physics priors by e.g. using neural ODEs for the latent state modeling.
POMDP just means "observations are not state" and that you need to use a stateful policy to infer the state somehow, but without further assumptions it's difficult to answer this question