Learning robot navigation strategies among pedestrian is crucial for domain based applications. Combining perception, planning and prediction allows us to model the interactions between robots and pedestrians, resulting in impressive outcomes especially with recent approaches based on deep reinforcement learning (RL). However, these works do not consider multi-robot scenarios. In this paper, we present MultiSoc, a new method for learning multi-agent socially aware navigation strategies using RL. Inspired by recent works on multi-agent deep RL, our method leverages graph-based representation of agent interactions, combining the positions and fields of view of entities (pedestrians and agents). Each agent uses a model based on two Graph Neural Network combined with attention mechanisms. First an edge-selector produces a sparse graph, then a crowd coordinator applies node attention to produce a graph representing the influence of each entity on the others. This is incorporated into a model-free RL framework to learn multi-agent policies. We evaluate our approach on simulation and provide a series of experiments in a set of various conditions (number of agents / pedestrians). Empirical results show that our method learns faster than social navigation deep RL mono-agent techniques, and enables efficient multi-agent implicit coordination in challenging crowd navigation with multiple heterogeneous humans. Furthermore, by incorporating customizable meta-parameters, we can adjust the neighborhood density to take into account in our navigation strategy.

翻译：在行人环境中学习机器人导航策略对于领域应用至关重要。结合感知、规划和预测能够建模机器人与行人间的交互，尤其基于深度强化学习(RL)的最新方法取得了令人瞩目的成果。然而，现有工作未考虑多机器人场景。本文提出MultiSoc，一种基于RL学习多智能体社交感知导航策略的新方法。受多智能体深度RL研究启发，该方法利用基于图的智能体交互表示，融合实体（行人与智能体）的位置与视野。每个智能体采用结合注意力机制的双图神经网络模型：边缘选择器首先生成稀疏图，人群协调器随后应用节点注意力生成表达各实体间相互影响的图。该结构被嵌入无模型RL框架以学习多智能体策略。我们在仿真环境中评估该方法，并在多组条件（智能体/行人数量）下进行系列实验。实证结果表明，相比社交导航深度RL单智能体技术，我们的方法学习速度更快，并在包含多个异质行人的挑战性人群导航中实现高效的多智能体隐式协同。此外，通过引入可定制元参数，我们能够调节导航策略中需考虑的邻域密度。