  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.
