Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Communication-Efficient Actor-Critic Methods for Homogeneous Markov
  Games   Recent success in cooperative multi-agent reinforcement learning (MARL)
relies on centralized training and policy sharing. Centralized training
eliminates the issue of non-stationarity MARL yet induces large communication
costs, and policy sharing is empirically crucial to efficient learning in
certain tasks yet lacks theoretical justification. In this paper, we formally
characterize a subclass of cooperative Markov games where agents exhibit a
certain form of homogeneity such that policy sharing provably incurs no
suboptimality. This enables us to develop the first consensus-based
decentralized actor-critic method where the consensus update is applied to both
the actors and the critics while ensuring convergence. We also develop
practical algorithms based on our decentralized actor-critic method to reduce
the communication cost during training, while still yielding policies
comparable with centralized training.

14
The categories of this paper are: math-ph
3
The categories of this paper are: physics
10
The categories of this paper are: q-bio
0
The categories of this paper are: hep-ph
4
The categories of this paper are: cond-mat
34
The categories of this paper are: chem-ph
33
The categories of this paper are: plasm-ph
29
The categories of this paper are: funct-an
13
The categories of this paper are: quant-ph
35
The categories of this paper are: mtrl-th
36
The categories of this paper are: cmp-lg
28
The categories of this paper are: alg-geom
11
The categories of this paper are: hep-lat
22
The categories of this paper are: chao-dyn
6
The categories of this paper are: astro-ph
15
The categories of this paper are: nucl-ex