The study of behavioral diversity in Multi-Agent Reinforcement Learning
(MARL) is a nascent yet promising field. In this context, the present work
deals with the question of how to control the diversity of a multi-agent
system. With no existing approaches to control diversity to a set value,
current solutions focus on blindly promoting it via intrinsic rewards or
additional loss functions, effectively changing the learning objective and
lacking a principled measure for it. To address this, we introduce Diversity
Control (DiCo), a method able to control diversity to an exact value of a given
metric by representing policies as the sum of a parameter-shared component and
dynamically scaled per-agent components. By applying constraints directly to
the policy architecture, DiCo leaves the learning objective unchanged, enabling
its applicability to any actor-critic MARL algorithm. We theoretically prove
that DiCo achieves the desired diversity, and we provide several experiments,
both in cooperative and competitive tasks, that show how DiCo can be employed
as a novel paradigm to increase performance and sample efficiency in MARL.
Multimedia results are available on the paper's website:
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多智能体强化学习中行为多样性的研究是一个新兴且有潜力的领域。本研究提出了一种名为 DiCo 的多样性控制方法，通过在策略架构中应用约束，能够在不改变学习目标的情况下精确控制多样性，从而增加多智能体强化学习算法的性能和样本利用率。

多智能体强化学习中的行为多样性控制

Controlling Behavioral Diversity in Multi-Agent Reinforcement Learning

Multi-view clustering aims at exploiting information from multiple
heterogeneous views to promote clustering. Most previous works search for only
one optimal clustering based on the predefined clustering criterion, but
devising such a criterion that captures what users need is difficult. Due to
the multiplicity of multi-view data, we can have meaningful alternative
clusterings. In addition, the incomplete multi-view data problem is ubiquitous
in real world but has not been studied for multiple clusterings. To address
these issues, we introduce a deep incomplete multi-view multiple clusterings
(DiMVMC) framework, which achieves the completion of data view and multiple
shared representations simultaneously by optimizing multiple groups of decoder
deep networks. In addition, it minimizes a redundancy term to simultaneously
%uses Hilbert-Schmidt Independence Criterion (HSIC) to control the diversity
among these representations and among parameters of different networks. Next,
it generates an individual clustering from each of these shared
representations. Experiments on benchmark datasets confirm that DiMVMC
outperforms the state-of-the-art competitors in generating multiple clusterings
with high diversity and quality.

本文提出了一个深度不完全多视图多聚类 (DiMVMC) 框架来处理多视图数据不完整问题，并通过优化多组解码器深度网络来实现数据视图和多个共享表示的同时完成。实验表明，DiMVMC 在生成高质量，多样性的多个聚类方面优于现有的同类竞争对手。