Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Policy Gradient With Value Function Approximation For Collective
  Multiagent Planning   Decentralized (PO)MDPs provide an expressive framework for sequential
decision making in a multiagent system. Given their computational complexity,
recent research has focused on tractable yet practical subclasses of
Dec-POMDPs. We address such a subclass called CDEC-POMDP where the collective
behavior of a population of agents affects the joint-reward and environment
dynamics. Our main contribution is an actor-critic (AC) reinforcement learning
method for optimizing CDEC-POMDP policies. Vanilla AC has slow convergence for
larger problems. To address this, we show how a particular decomposition of the
approximate action-value function over agents leads to effective updates, and
also derive a new way to train the critic based on local reward signals.
Comparisons on a synthetic benchmark and a real-world taxi fleet optimization
problem show that our new AC approach provides better quality solutions than
previous best approaches.

24
The categories of this paper are: dg-ga
17
The categories of this paper are: q-fin
33
The categories of this paper are: plasm-ph
20
The categories of this paper are: acc-phys
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The categories of this paper are: stat
26
The categories of this paper are: bayes-an
12
The categories of this paper are: nucl-th
5
The categories of this paper are: gr-qc
3
The categories of this paper are: physics
8
The categories of this paper are: hep-ex
9
The categories of this paper are: nlin
19
The categories of this paper are: eess
15
The categories of this paper are: nucl-ex
18
The categories of this paper are: econ
28
The categories of this paper are: alg-geom
4
The categories of this paper are: cond-mat