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。