Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Containerized Distributed Value-Based Multi-Agent Reinforcement Learning   Multi-agent reinforcement learning tasks put a high demand on the volume of
training samples. Different from its single-agent counterpart, distributed
value-based multi-agent reinforcement learning faces the unique challenges of
demanding data transfer, inter-process communication management, and high
requirement of exploration. We propose a containerized learning framework to
solve these problems. We pack several environment instances, a local learner
and buffer, and a carefully designed multi-queue manager which avoids blocking
into a container. Local policies of each container are encouraged to be as
diverse as possible, and only trajectories with highest priority are sent to a
global learner. In this way, we achieve a scalable, time-efficient, and diverse
distributed MARL learning framework with high system throughput. To own
knowledge, our method is the first to solve the challenging Google Research
Football full game $5\_v\_5$. On the StarCraft II micromanagement benchmark,
our method gets $4$-$18\times$ better results compared to state-of-the-art
non-distributed MARL algorithms.

7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph
6
The categories of this paper are: astro-ph
30
The categories of this paper are: q-alg
8
The categories of this paper are: hep-ex
9
The categories of this paper are: nlin
25
The categories of this paper are: patt-sol
21
The categories of this paper are: adap-org
22
The categories of this paper are: chao-dyn
35
The categories of this paper are: mtrl-th
17
The categories of this paper are: q-fin
12
The categories of this paper are: nucl-th
4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
26
The categories of this paper are: bayes-an
27
The categories of this paper are: comp-gas