Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Coordination-driven learning in multi-agent problem spaces   We discuss the role of coordination as a direct learning objective in
multi-agent reinforcement learning (MARL) domains. To this end, we present a
novel means of quantifying coordination in multi-agent systems, and discuss the
implications of using such a measure to optimize coordinated agent policies.
This concept has important implications for adversary-aware RL, which we take
to be a sub-domain of multi-agent learning.

8
The categories of this paper are: hep-ex
31
The categories of this paper are: ao-sci
18
The categories of this paper are: econ
32
The categories of this paper are: atom-ph
30
The categories of this paper are: q-alg
23
The categories of this paper are: solv-int
15
The categories of this paper are: nucl-ex
16
The categories of this paper are: stat
19
The categories of this paper are: eess
5
The categories of this paper are: gr-qc
0
The categories of this paper are: hep-ph
22
The categories of this paper are: chao-dyn
11
The categories of this paper are: hep-lat
33
The categories of this paper are: plasm-ph
13
The categories of this paper are: quant-ph
25
The categories of this paper are: patt-sol