Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings. (arXiv:2206.12081v1 [cs.LG])

We study reinforcement learning with function approximation for large-scale
Partially Observable Markov Decision Processes (POMDPs) where the state space
and observation space are large or even continuous. Particularly, we consider
Hilbert space embeddings of POMDP where the feature of latent states and the
feature of observations admit a conditional Hilbert space embedding of the
observation emission process, and the latent state transition is deterministic.
Under the function approximation setup where the optimal latent state-action
$Q$-function is linear in the state …

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