Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Feature-Based Q-Learning for Two-Player Stochastic Games   Consider a two-player zero-sum stochastic game where the transition function
can be embedded in a given feature space. We propose a two-player Q-learning
algorithm for approximating the Nash equilibrium strategy via sampling. The
algorithm is shown to find an $\epsilon$-optimal strategy using sample size
linear to the number of features. To further improve its sample efficiency, we
develop an accelerated algorithm by adopting techniques such as variance
reduction, monotonicity preservation and two-sided strategy approximation. We
prove that the algorithm is guaranteed to find an $\epsilon$-optimal strategy
using no more than $\tilde{\mathcal{O}}(K/(\epsilon^{2}(1-\gamma)^{4}))$
samples with high probability, where $K$ is the number of features and $\gamma$
is a discount factor. The sample, time and space complexities of the algorithm
are independent of original dimensions of the game.

32
The categories of this paper are: atom-ph
7
The categories of this paper are: hep-th
17
The categories of this paper are: q-fin
10
The categories of this paper are: q-bio
33
The categories of this paper are: plasm-ph
23
The categories of this paper are: solv-int
14
The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
36
The categories of this paper are: cmp-lg
29
The categories of this paper are: funct-an
13
The categories of this paper are: quant-ph
5
The categories of this paper are: gr-qc
24
The categories of this paper are: dg-ga
20
The categories of this paper are: acc-phys
9
The categories of this paper are: nlin
8
The categories of this paper are: hep-ex