Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On Oracle-Efficient PAC RL with Rich Observations   We study the computational tractability of PAC reinforcement learning with
rich observations. We present new provably sample-efficient algorithms for
environments with deterministic hidden state dynamics and stochastic rich
observations. These methods operate in an oracle model of computation --
accessing policy and value function classes exclusively through standard
optimization primitives -- and therefore represent computationally efficient
alternatives to prior algorithms that require enumeration. With stochastic
hidden state dynamics, we prove that the only known sample-efficient algorithm,
OLIVE, cannot be implemented in the oracle model. We also present several
examples that illustrate fundamental challenges of tractable PAC reinforcement
learning in such general settings.

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The categories of this paper are: acc-phys
35
The categories of this paper are: mtrl-th
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The categories of this paper are: atom-ph
14
The categories of this paper are: math-ph
0
The categories of this paper are: hep-ph
18
The categories of this paper are: econ
2
The categories of this paper are: math
19
The categories of this paper are: eess
29
The categories of this paper are: funct-an
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The categories of this paper are: chao-dyn
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The categories of this paper are: quant-ph
12
The categories of this paper are: nucl-th
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The categories of this paper are: q-fin
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The categories of this paper are: bayes-an
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The categories of this paper are: supr-con
27
The categories of this paper are: comp-gas