Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Q-CP: Learning Action Values for Cooperative Planning   Research on multi-robot systems has demonstrated promising results in
manifold applications and domains. Still, efficiently learning an effective
robot behaviors is very difficult, due to unstructured scenarios, high
uncertainties, and large state dimensionality (e.g. hyper-redundant and groups
of robot). To alleviate this problem, we present Q-CP a cooperative model-based
reinforcement learning algorithm, which exploits action values to both (1)
guide the exploration of the state space and (2) generate effective policies.
Specifically, we exploit Q-learning to attack the curse-of-dimensionality in
the iterations of a Monte-Carlo Tree Search. We implement and evaluate Q-CP on
different stochastic cooperative (general-sum) games: (1) a simple cooperative
navigation problem among 3 robots, (2) a cooperation scenario between a pair of
KUKA YouBots performing hand-overs, and (3) a coordination task between two
mobile robots entering a door. The obtained results show the effectiveness of
Q-CP in the chosen applications, where action values drive the exploration and
reduce the computational demand of the planning process while achieving good
performance.

5
The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th
8
The categories of this paper are: hep-ex
25
The categories of this paper are: patt-sol
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
9
The categories of this paper are: nlin
23
The categories of this paper are: solv-int
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
29
The categories of this paper are: funct-an
2
The categories of this paper are: math
26
The categories of this paper are: bayes-an
19
The categories of this paper are: eess
7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph