Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Monte Carlo Bayesian Reinforcement Learning   Bayesian reinforcement learning (BRL) encodes prior knowledge of the world in
a model and represents uncertainty in model parameters by maintaining a
probability distribution over them. This paper presents Monte Carlo BRL
(MC-BRL), a simple and general approach to BRL. MC-BRL samples a priori a
finite set of hypotheses for the model parameter values and forms a discrete
partially observable Markov decision process (POMDP) whose state space is a
cross product of the state space for the reinforcement learning task and the
sampled model parameter space. The POMDP does not require conjugate
distributions for belief representation, as earlier works do, and can be solved
relatively easily with point-based approximation algorithms. MC-BRL naturally
handles both fully and partially observable worlds. Theoretical and
experimental results show that the discrete POMDP approximates the underlying
BRL task well with guaranteed performance.

24
The categories of this paper are: dg-ga
30
The categories of this paper are: q-alg
8
The categories of this paper are: hep-ex
19
The categories of this paper are: eess
21
The categories of this paper are: adap-org
20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
33
The categories of this paper are: plasm-ph
32
The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph
13
The categories of this paper are: quant-ph
23
The categories of this paper are: solv-int
7
The categories of this paper are: hep-th
35
The categories of this paper are: mtrl-th
18
The categories of this paper are: econ