Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Reinforcement Learning via AIXI Approximation   This paper introduces a principled approach for the design of a scalable
general reinforcement learning agent. This approach is based on a direct
approximation of AIXI, a Bayesian optimality notion for general reinforcement
learning agents. Previously, it has been unclear whether the theory of AIXI
could motivate the design of practical algorithms. We answer this hitherto open
question in the affirmative, by providing the first computationally feasible
approximation to the AIXI agent. To develop our approximation, we introduce a
Monte Carlo Tree Search algorithm along with an agent-specific extension of the
Context Tree Weighting algorithm. Empirically, we present a set of encouraging
results on a number of stochastic, unknown, and partially observable domains.

20
The categories of this paper are: acc-phys
24
The categories of this paper are: dg-ga
30
The categories of this paper are: q-alg
25
The categories of this paper are: patt-sol
13
The categories of this paper are: quant-ph
4
The categories of this paper are: cond-mat
21
The categories of this paper are: adap-org
33
The categories of this paper are: plasm-ph
14
The categories of this paper are: math-ph
12
The categories of this paper are: nucl-th
11
The categories of this paper are: hep-lat
2
The categories of this paper are: math
17
The categories of this paper are: q-fin
31
The categories of this paper are: ao-sci
23
The categories of this paper are: solv-int
37
The categories of this paper are: supr-con