This paper explores the emergent behaviors in multi-agent environments, specifically focusing on dynamics like hide-and-seek, highlighting how complex behaviors can naturally arise from simple rules and interactions in a structured learning framework. © OpenAI

This paper presents a learning framework using deep reinforcement learning to develop control policies for physically simulated athletes in sports like boxing and fencing. © Meta Research

This paper provides an improved approach in simulating elastic objects, such as hair, rubber and skin, as well as their two-way coupled interactions with other materials. © ACM