论文标题
强大的多机构强化学习与社会能力进行协调和沟通
Robust Multi-Agent Reinforcement Learning with Social Empowerment for Coordination and Communication
论文作者
论文摘要
我们考虑合作交流和协调任务的强大多机构增强学习（MARL）的问题。 MARL代理人（主要是以集中式训练的人）可能会变得脆弱，因为他们可以采用在期望其他代理人将以某种方式行事而不是对其行为做出反应的政策。我们的目标是将学习过程偏向寻找对他人行为的反应的策略。社会授权衡量代理人行动之间的潜在影响。我们将其作为额外的奖励术语，因此代理更好地适应其他代理商的行为。我们表明，所提出的方法导致在三个合作交流和协调任务中获得更高的奖励和更高的成功率。
We consider the problem of robust multi-agent reinforcement learning (MARL) for cooperative communication and coordination tasks. MARL agents, mainly those trained in a centralized way, can be brittle because they can adopt policies that act under the expectation that other agents will act a certain way rather than react to their actions. Our objective is to bias the learning process towards finding strategies that remain reactive towards others' behavior. Social empowerment measures the potential influence between agents' actions. We propose it as an additional reward term, so agents better adapt to other agents' actions. We show that the proposed method results in obtaining higher rewards faster and a higher success rate in three cooperative communication and coordination tasks.
