Learning Graph-Enhanced Commander-Executor for Multi-Agent Navigation

• URL: http://arxiv.org/abs/2302.04094v1
• Date: Wed, 8 Feb 2023 14:44:21 GMT
• Title: Learning Graph-Enhanced Commander-Executor for Multi-Agent Navigation
• Authors: Xinyi Yang, Shiyu Huang, Yiwen Sun, Yuxiang Yang, Chao Yu, Wei-Wei Tu, Huazhong Yang, Yu Wang
• Abstract summary: Multi-agent reinforcement learning (MARL) has shown promising results for solving this issue. Goal-conditioned hierarchical reinforcement learning (HRL) provides a promising direction to tackle this challenge. We propose MAGE-X, a graph-based goal-conditioned hierarchical method for multi-agent navigation tasks.
• Score: 28.71585436726336
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper investigates the multi-agent navigation problem, which requires multiple agents to reach the target goals in a limited time. Multi-agent reinforcement learning (MARL) has shown promising results for solving this issue. However, it is inefficient for MARL to directly explore the (nearly) optimal policy in the large search space, which is exacerbated as the agent number increases (e.g., 10+ agents) or the environment is more complex (e.g., 3D simulator). Goal-conditioned hierarchical reinforcement learning (HRL) provides a promising direction to tackle this challenge by introducing a hierarchical structure to decompose the search space, where the low-level policy predicts primitive actions in the guidance of the goals derived from the high-level policy. In this paper, we propose Multi-Agent Graph-Enhanced Commander-Executor (MAGE-X), a graph-based goal-conditioned hierarchical method for multi-agent navigation tasks. MAGE-X comprises a high-level Goal Commander and a low-level Action Executor. The Goal Commander predicts the probability distribution of goals and leverages them to assign each agent the most appropriate final target. The Action Executor utilizes graph neural networks (GNN) to construct a subgraph for each agent that only contains crucial partners to improve cooperation. Additionally, the Goal Encoder in the Action Executor captures the relationship between the agent and the designated goal to encourage the agent to reach the final target. The results show that MAGE-X outperforms the state-of-the-art MARL baselines with a 100% success rate with only 3 million training steps in multi-agent particle environments (MPE) with 50 agents, and at least a 12% higher success rate and 2x higher data efficiency in a more complicated quadrotor 3D navigation task.

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