Safe and efficient co-planning of multiple robots in pedestrian participation
environments is promising for applications. In this work, a novel multi-robot
social-aware efficient cooperative planner that on the basis of off-policy
multi-agent reinforcement learning (MARL) under partial dimension-varying
observation and imperfect perception conditions is proposed. We adopt
temporal-spatial graph (TSG)-based social encoder to better extract the
importance of social relation between each robot and the pedestrians in its
field of view (FOV). Also, we introduce K-step lookahead reward setting in
multi-robot RL framework to avoid aggressive, intrusive, short-sighted, and
unnatural motion decisions generated by robots. Moreover, we improve the
traditional centralized critic network with multi-head global attention module
to better aggregates local observation information among different robots to
guide the process of individual policy update. Finally, multi-group
experimental results verify the effectiveness of the proposed cooperative
motion planner.

本文提出了一种基于离线多智能体强化学习（MARL）的多机器人社交感知和高效协同规划方法，使用时间 - 空间图（TSG）进行社交编码，引入 K 步先见奖励设置，并改进了传统的集中式批判者网络，从而在多群组实验中验证了该方法的有效性。

多机器人社交感知协作规划在行人环境中的多智能体强化学习

Multi-robot Social-aware Cooperative Planning in Pedestrian Environments Using Multi-agent Reinforcement Learning

Being able to recommend links between users in online social networks is
important for users to connect with like-minded individuals as well as for the
platforms themselves and third parties leveraging social media information to
grow their business. Predictions are typically based on unsupervised or
supervised learning, often leveraging simple yet effective graph topological
information, such as the number of common neighbors. However, we argue that
richer information about personal social structure of individuals might lead to
better predictions. In this paper, we propose to leverage well-established
social cognitive theories to improve link prediction performance. According to
these theories, individuals arrange their social relationships along, on
average, five concentric circles of decreasing intimacy. We postulate that
relationships in different circles have different importance in predicting new
links. In order to validate this claim, we focus on popular feature-extraction
prediction algorithms (both unsupervised and supervised) and we extend them to
include social-circles awareness. We validate the prediction performance of
these circle-aware algorithms against several benchmarks (including their
baseline versions as well as node-embedding- and GNN-based link prediction),
leveraging two Twitter datasets comprising a community of video gamers and
generic users. We show that social-awareness generally provides significant
improvements in the prediction performance, beating also state-of-the-art
solutions like node2vec and SEAL, and without increasing the computational
complexity. Finally, we show that social-awareness can be used in place of
using a classifier (which may be costly or impractical) for targeting a
specific category of users.

本文提出利用社交认知理论来改善在在线社交网络中的链接预测算法，并探讨了利用社交圈子意识来扩展特征提取算法并验证其预测性能的方法，在社交圈子意识的算法中，预测性能得到了显著的提升，打败了一些最先进的解决方案，同时也可以用于针对特定类别的用户进行定向营销。