Multi-agent teaming achieves better performance when there is communication
among participating agents allowing them to coordinate their actions for
maximizing shared utility. However, when collaborating a team of agents with
different action and observation spaces, information sharing is not
straightforward and requires customized communication protocols, depending on
sender and receiver types. Without properly modeling such heterogeneity in
agents, communication becomes less helpful and could even deteriorate the
multi-agent cooperation performance. We propose heterogeneous graph attention
networks, called HetNet, to learn efficient and diverse communication models
for coordinating heterogeneous agents towards accomplishing tasks that are of
collaborative nature. We propose a Multi-Agent Heterogeneous Actor-Critic
(MAHAC) learning paradigm to obtain collaborative per-class policies and
effective communication protocols for composite robot teams. Our proposed
framework is evaluated against multiple baselines in a complex environment in
which agents of different types must communicate and cooperate to satisfy the
objectives. Experimental results show that HetNet outperforms the baselines in
learning sophisticated multi-agent communication protocols by achieving
$\sim$10\% improvements in performance metrics.

该研究提出了一种名为 HetNet 的异构网络注意力思想，用于协调不同类型的机器人团队完成合作任务，并评估 HetNet 在解决信息交互问题中的性能提升，实验结果表明其相比基准方法，在学习复杂多智能体协作的通信协议方面，表现更好。

异构图注意力网络学习多样化通信

Heterogeneous Graph Attention Networks for Learning Diverse  Communication

We consider the problem of learning high-dimensional, nonparametric and
structured (e.g. Gaussian) distributions in distributed networks, where each
node in the network observes an independent sample from the underlying
distribution and can use $k$ bits to communicate its sample to a central
processor. We consider three different models for communication. Under the
independent model, each node communicates its sample to a central processor by
independently encoding it into $k$ bits. Under the more general sequential or
blackboard communication models, nodes can share information interactively but
each node is restricted to write at most $k$ bits on the final transcript. We
characterize the impact of the communication constraint $k$ on the minimax risk
of estimating the underlying distribution under $\ell^2$ loss. We develop
minimax lower bounds that apply in a unified way to many common statistical
models and reveal that the impact of the communication constraint can be
qualitatively different depending on the tail behavior of the score function
associated with each model. A key ingredient in our proofs is a geometric
characterization of Fisher information from quantized samples.

研究如何在分布式网络中学习高维、非参数和结构化（如高斯）分布，并考虑不同通信模型（包括独立、顺序和黑板模型）的交互限制对于最小化风险和 Fisher 信息的影响。

通过费舍尔信息在通信约束下学习分布的下界

Lower Bounds for Learning Distributions under Communication Constraints  via Fisher Information

Motivated by sensor networks and other distributed settings, several models
for distributed learning are presented. The models differ from classical works
in statistical pattern recognition by allocating observations of an independent
and identically distributed (i.i.d.) sampling process amongst members of a
network of simple learning agents. The agents are limited in their ability to
communicate to a central fusion center and thus, the amount of information
available for use in classification or regression is constrained. For several
basic communication models in both the binary classification and regression
frameworks, we question the existence of agent decision rules and fusion rules
that result in a universally consistent ensemble. The answers to this question
present new issues to consider with regard to universal consistency. Insofar as
these models present a useful picture of distributed scenarios, this paper
addresses the issue of whether or not the guarantees provided by Stone's
Theorem in centralized environments hold in distributed settings.

本文探讨了在分布式环境中统计模式识别的几个不同模型，着重考虑了通信模型和智能学习体系结构对于智能体决策规则和融合规则的影响，同时探索了 Stone 定理在分布式环境中的适用性。