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

The dm_control software package is a collection of Python libraries and task suites for
reinforcement learning agents in an articulated-body simulation. Infrastructure includes a …

Multi-agent reinforcement learning (MARL) has been a rapidly evolving field. This paper
presents a comprehensive survey of MARL and its applications. We trace the historical …

The comprehension of how local interactions arise in global collective behavior is of utmost
importance in both biological and physical research. Traditional agent-based models often …

Abstract Large Language Models (LLMs) have taken the field of AI by storm, but their
adoption in the field of Artificial Life (ALife) has been, so far, relatively reserved. In this work …

Collective self-organization of animal groups is a recurring phenomenon in nature which
has attracted a lot of attention in natural and social sciences. To understand how collective …

The nature-inspired behavior of collective motion is found to be an optimal solution in
swarming systems for predator avoidance and survival. In this work, we propose a two-level …

Multiagent coordination is highly desirable with many uses in a variety of tasks. In nature, the
phenomenon of coordinated flocking is highly common with applications related to …

Complex collective motion patterns can emerge from very simple local interactions among
individual agents. However, it is still unclear how and why the interactions among …

Ecosystem models can be used for understanding general phenomena of evolution,
ecology, and ethology. They can also be used for analyzing and predicting the ecological …