Cooperation in multi-agent learning (MAL) is a topic at the intersection of numerous
disciplines, including game theory, economics, social sciences, and evolutionary biology …

Existing evaluation suites for multi-agent reinforcement learning (MARL) do not assess
generalization to novel situations as their primary objective (unlike supervised learning …

Society is characterized by the presence of a variety of social norms: collective patterns of
sanctioning that can prevent miscoordination and free-riding. Inspired by this, we aim to …

Multi-agent reinforcement learning (MARL) is a powerful tool for training automated systems
acting independently in a common environment. However, it can lead to sub-optimal …

In multi-agent partially observable sequential decision problems with general-sum rewards,
it is necessary to account for the egoism (individual rewards), utilitarianism (social welfare) …

In recent years, deep learning methods have shown promising capabilities for extracting
informative and discriminative features from electroencephalography (EEG) data. However …

Abstract Multi-agent Reinforcement Learning (MARL) is a powerful tool for training
autonomous agents acting independently in a common environment. However, it can lead to …

In multi-agent settings with mixed incentives, methods developed for zero-sum games have
been shown to lead to detrimental outcomes. To address this issue, opponent shaping (OS) …

A major challenge in cognitive science and AI has been to understand how intelligent
autonomous agents might acquire and predict the behavioral and mental states of other …

Cooperation in settings where agents have both common and conflicting interests (mixed-
motive environments) has recently received considerable attention in multi-agent learning …