Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: HAVEN: Hierarchical Cooperative Multi-Agent Reinforcement Learning with
  Dual Coordination Mechanism   Multi-agent reinforcement learning often suffers from the exponentially
larger action space caused by a large number of agents. In this paper, we
propose a novel value decomposition framework HAVEN based on hierarchical
reinforcement learning for the fully cooperative multi-agent problems. In order
to address instabilities that arise from the concurrent optimization of
high-level and low-level policies and another concurrent optimization of
agents, we introduce the dual coordination mechanism of inter-layer strategies
and inter-agent strategies. HAVEN does not require domain knowledge and
pretraining at all, and can be applied to any value decomposition variants. Our
method is demonstrated to achieve superior results to many baselines on
StarCraft II micromanagement tasks and offers an efficient solution to
multi-agent hierarchical reinforcement learning in fully cooperative scenarios.

11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph
33
The categories of this paper are: plasm-ph
36
The categories of this paper are: cmp-lg
17
The categories of this paper are: q-fin
14
The categories of this paper are: math-ph
7
The categories of this paper are: hep-th
10
The categories of this paper are: q-bio
24
The categories of this paper are: dg-ga
8
The categories of this paper are: hep-ex
5
The categories of this paper are: gr-qc
31
The categories of this paper are: ao-sci
4
The categories of this paper are: cond-mat
23
The categories of this paper are: solv-int
25
The categories of this paper are: patt-sol