Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Coding for Distributed Multi-Agent Reinforcement Learning   This paper aims to mitigate straggler effects in synchronous distributed
learning for multi-agent reinforcement learning (MARL) problems. Stragglers
arise frequently in a distributed learning system, due to the existence of
various system disturbances such as slow-downs or failures of compute nodes and
communication bottlenecks. To resolve this issue, we propose a coded
distributed learning framework, which speeds up the training of MARL algorithms
in the presence of stragglers, while maintaining the same accuracy as the
centralized approach. As an illustration, a coded distributed version of the
multi-agent deep deterministic policy gradient(MADDPG) algorithm is developed
and evaluated. Different coding schemes, including maximum distance separable
(MDS)code, random sparse code, replication-based code, and regular low density
parity check (LDPC) code are also investigated. Simulations in several
multi-robot problems demonstrate the promising performance of the proposed
framework.

16
The categories of this paper are: stat
29
The categories of this paper are: funct-an
6
The categories of this paper are: astro-ph
5
The categories of this paper are: gr-qc
21
The categories of this paper are: adap-org
25
The categories of this paper are: patt-sol
22
The categories of this paper are: chao-dyn
14
The categories of this paper are: math-ph
15
The categories of this paper are: nucl-ex
4
The categories of this paper are: cond-mat
10
The categories of this paper are: q-bio
9
The categories of this paper are: nlin
2
The categories of this paper are: math
13
The categories of this paper are: quant-ph
23
The categories of this paper are: solv-int
11
The categories of this paper are: hep-lat