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- W2620405877 abstract "We study the computational complexity of the infinite-horizon discounted-reward Markov Decision Problem (MDP) with a finite state space $|mathcal{S}|$ and a finite action space $|mathcal{A}|$. We show that any randomized algorithm needs a running time at least $Omega(|mathcal{S}|^2|mathcal{A}|)$ to compute an $epsilon$-optimal policy with high probability. We consider two variants of the MDP where the input is given in specific data structures, including arrays of cumulative probabilities and binary trees of transition probabilities. For these cases, we show that the complexity lower bound reduces to $Omegaleft( frac{|mathcal{S}| |mathcal{A}|}{epsilon} right)$. These results reveal a surprising observation that the computational complexity of the MDP depends on the data structure of input." @default.
- W2620405877 created "2017-06-05" @default.
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- W2620405877 date "2017-05-20" @default.
- W2620405877 modified "2023-09-27" @default.
- W2620405877 title "Lower Bound On the Computational Complexity of Discounted Markov Decision Problems" @default.
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- W2620405877 doi "https://doi.org/10.48550/arxiv.1705.07312" @default.
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