Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Exploring the Benefits of Teams in Multiagent Learning   For problems requiring cooperation, many multiagent systems implement
solutions among either individual agents or across an entire population towards
a common goal. Multiagent teams are primarily studied when in conflict;
however, organizational psychology (OP) highlights the benefits of teams among
human populations for learning how to coordinate and cooperate. In this paper,
we propose a new model of multiagent teams for reinforcement learning (RL)
agents inspired by OP and early work on teams in artificial intelligence. We
validate our model using complex social dilemmas that are popular in recent
multiagent RL and find that agents divided into teams develop cooperative
pro-social policies despite incentives to not cooperate. Furthermore, agents
are better able to coordinate and learn emergent roles within their teams and
achieve higher rewards compared to when the interests of all agents are
aligned.

6
The categories of this paper are: astro-ph
17
The categories of this paper are: q-fin
27
The categories of this paper are: comp-gas
26
The categories of this paper are: bayes-an
4
The categories of this paper are: cond-mat
21
The categories of this paper are: adap-org
14
The categories of this paper are: math-ph
24
The categories of this paper are: dg-ga
25
The categories of this paper are: patt-sol
22
The categories of this paper are: chao-dyn
12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
0
The categories of this paper are: hep-ph
15
The categories of this paper are: nucl-ex
2
The categories of this paper are: math
16
The categories of this paper are: stat