HAVEN: Hierarchical Cooperative Multi-Agent Reinforcement Learning with
Dual Coordination Mechanism
- URL: http://arxiv.org/abs/2110.07246v1
- Date: Thu, 14 Oct 2021 10:43:47 GMT
- Title: HAVEN: Hierarchical Cooperative Multi-Agent Reinforcement Learning with
Dual Coordination Mechanism
- Authors: Zhiwei Xu, Yunpeng Bai, Bin Zhang, Dapeng Li, Guoliang Fan
- Abstract summary: Multi-agent reinforcement learning often suffers from the exponentially larger action space caused by a large number of agents.
We propose a novel value decomposition framework HAVEN based on hierarchical reinforcement learning for the fully cooperative multi-agent problems.
- Score: 17.993973801986677
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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