Soft Hierarchical Graph Recurrent Networks for Many-Agent Partially
Observable Environments
- URL: http://arxiv.org/abs/2109.02032v1
- Date: Sun, 5 Sep 2021 09:51:25 GMT
- Title: Soft Hierarchical Graph Recurrent Networks for Many-Agent Partially
Observable Environments
- Authors: Zhenhui Ye, Xiaohong Jiang, Guanghua Song, Bowei Yang
- Abstract summary: We propose a novel network structure called hierarchical graph recurrent network(HGRN) for multi-agent cooperation under partial observability.
Based on the above technologies, we proposed a value-based MADRL algorithm called Soft-HGRN and its actor-critic variant named SAC-HRGN.
- Score: 9.067091068256747
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The recent progress in multi-agent deep reinforcement learning(MADRL) makes
it more practical in real-world tasks, but its relatively poor scalability and
the partially observable constraints raise challenges to its performance and
deployment. Based on our intuitive observation that the human society could be
regarded as a large-scale partially observable environment, where each
individual has the function of communicating with neighbors and remembering its
own experience, we propose a novel network structure called hierarchical graph
recurrent network(HGRN) for multi-agent cooperation under partial
observability. Specifically, we construct the multi-agent system as a graph,
use the hierarchical graph attention network(HGAT) to achieve communication
between neighboring agents, and exploit GRU to enable agents to record
historical information. To encourage exploration and improve robustness, we
design a maximum-entropy learning method to learn stochastic policies of a
configurable target action entropy. Based on the above technologies, we
proposed a value-based MADRL algorithm called Soft-HGRN and its actor-critic
variant named SAC-HRGN. Experimental results based on three homogeneous tasks
and one heterogeneous environment not only show that our approach achieves
clear improvements compared with four baselines, but also demonstrates the
interpretability, scalability, and transferability of the proposed model.
Ablation studies prove the function and necessity of each component.
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