Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Bi-directional Value Learning for Risk-aware Planning Under Uncertainty:
  Extended Version   Decision-making under uncertainty is a crucial ability for autonomous
systems. In its most general form, this problem can be formulated as a
Partially Observable Markov Decision Process (POMDP). The solution policy of a
POMDP can be implicitly encoded as a value function. In partially observable
settings, the value function is typically learned via forward simulation of the
system evolution. Focusing on accurate and long-range risk assessment, we
propose a novel method, where the value function is learned in different phases
via a bi-directional search in belief space. A backward value learning process
provides a long-range and risk-aware base policy. A forward value learning
process ensures local optimality and updates the policy via forward
simulations. We consider a class of scalable and continuous-space rover
navigation problems (RNP) to assess the safety, scalability, and optimality of
the proposed algorithm. The results demonstrate the capabilities of the
proposed algorithm in evaluating long-range risk/safety of the planner while
addressing continuous problems with long planning horizons.

18
The categories of this paper are: econ
28
The categories of this paper are: alg-geom
2
The categories of this paper are: math
16
The categories of this paper are: stat
25
The categories of this paper are: patt-sol
23
The categories of this paper are: solv-int
8
The categories of this paper are: hep-ex
32
The categories of this paper are: atom-ph
3
The categories of this paper are: physics
12
The categories of this paper are: nucl-th
17
The categories of this paper are: q-fin
22
The categories of this paper are: chao-dyn
5
The categories of this paper are: gr-qc
26
The categories of this paper are: bayes-an
7
The categories of this paper are: hep-th
31
The categories of this paper are: ao-sci