Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning to Communicate with Deep Multi-Agent Reinforcement Learning   We consider the problem of multiple agents sensing and acting in environments
with the goal of maximising their shared utility. In these environments, agents
must learn communication protocols in order to share information that is needed
to solve the tasks. By embracing deep neural networks, we are able to
demonstrate end-to-end learning of protocols in complex environments inspired
by communication riddles and multi-agent computer vision problems with partial
observability. We propose two approaches for learning in these domains:
Reinforced Inter-Agent Learning (RIAL) and Differentiable Inter-Agent Learning
(DIAL). The former uses deep Q-learning, while the latter exploits the fact
that, during learning, agents can backpropagate error derivatives through
(noisy) communication channels. Hence, this approach uses centralised learning
but decentralised execution. Our experiments introduce new environments for
studying the learning of communication protocols and present a set of
engineering innovations that are essential for success in these domains.

10
The categories of this paper are: q-bio
27
The categories of this paper are: comp-gas
19
The categories of this paper are: eess
34
The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org
18
The categories of this paper are: econ
6
The categories of this paper are: astro-ph
25
The categories of this paper are: patt-sol
32
The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc
15
The categories of this paper are: nucl-ex
0
The categories of this paper are: hep-ph
35
The categories of this paper are: mtrl-th
2
The categories of this paper are: math
26
The categories of this paper are: bayes-an
22
The categories of this paper are: chao-dyn