Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Mixed Cooperative-Competitive Communication Using Multi-Agent
  Reinforcement Learning   By using communication between multiple agents in multi-agent environments,
one can reduce the effects of partial observability by combining one agent's
observation with that of others in the same dynamic environment. While a lot of
successful research has been done towards communication learning in cooperative
settings, communication learning in mixed cooperative-competitive settings is
also important and brings its own complexities such as the opposing team
overhearing the communication. In this paper, we apply differentiable
inter-agent learning (DIAL), designed for cooperative settings, to a mixed
cooperative-competitive setting. We look at the difference in performance
between communication that is private for a team and communication that can be
overheard by the other team. Our research shows that communicating agents are
able to achieve similar performance to fully observable agents after a given
training period in our chosen environment. Overall, we find that sharing
communication across teams results in decreased performance for the
communicating team in comparison to results achieved with private
communication.

20
The categories of this paper are: acc-phys
32
The categories of this paper are: atom-ph
15
The categories of this paper are: nucl-ex
19
The categories of this paper are: eess
16
The categories of this paper are: stat
26
The categories of this paper are: bayes-an
3
The categories of this paper are: physics
2
The categories of this paper are: math
9
The categories of this paper are: nlin
10
The categories of this paper are: q-bio
17
The categories of this paper are: q-fin
23
The categories of this paper are: solv-int
24
The categories of this paper are: dg-ga
14
The categories of this paper are: math-ph
11
The categories of this paper are: hep-lat
27
The categories of this paper are: comp-gas