Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DOP: Deep Optimistic Planning with Approximate Value Function Evaluation   Research on reinforcement learning has demonstrated promising results in
manifold applications and domains. Still, efficiently learning effective robot
behaviors is very difficult, due to unstructured scenarios, high uncertainties,
and large state dimensionality (e.g. multi-agent systems or hyper-redundant
robots). To alleviate this problem, we present DOP, a deep model-based
reinforcement learning algorithm, which exploits action values to both (1)
guide the exploration of the state space and (2) plan effective policies.
Specifically, we exploit deep neural networks to learn Q-functions that are
used to attack the curse of dimensionality during a Monte-Carlo tree search.
Our algorithm, in fact, constructs upper confidence bounds on the learned value
function to select actions optimistically. We implement and evaluate DOP on
different scenarios: (1) a cooperative navigation problem, (2) a fetching task
for a 7-DOF KUKA robot, and (3) a human-robot handover with a humanoid robot
(both in simulation and real). The obtained results show the effectiveness of
DOP in the chosen applications, where action values drive the exploration and
reduce the computational demand of the planning process while achieving good
performance.

2
The categories of this paper are: math
23
The categories of this paper are: solv-int
16
The categories of this paper are: stat
12
The categories of this paper are: nucl-th
28
The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th
26
The categories of this paper are: bayes-an
20
The categories of this paper are: acc-phys
10
The categories of this paper are: q-bio
0
The categories of this paper are: hep-ph
11
The categories of this paper are: hep-lat
7
The categories of this paper are: hep-th
15
The categories of this paper are: nucl-ex
4
The categories of this paper are: cond-mat
17
The categories of this paper are: q-fin