For a long time, researchers have sought artificial intelligence (AI) that matches or exceeds
human intelligence. AI agents, which are artificial entities capable of sensing the …

Abstract Deep Reinforcement Learning has made significant progress in multi-agent
systems in recent years. The aim of this review article is to provide an overview of recent …

The rapid advancement of chat-based language models has led to remarkable progress in
complex task-solving. However, their success heavily relies on human input to guide the …

Despite much progress in training artificial intelligence (AI) systems to imitate human
language, building agents that use language to communicate intentionally with humans in …

Compute, data, and algorithmic advances are the three fundamental factors that drive
progress in modern Machine Learning (ML). In this paper we study trends in the most readily …

Abstract Proximal Policy Optimization (PPO) is a ubiquitous on-policy reinforcement learning
algorithm but is significantly less utilized than off-policy learning algorithms in multi-agent …

One of the fundamental quests of AI is to produce agents that coordinate well with humans.
This problem is challenging, especially in domains that lack high quality human behavioral …

This paper introduces the PettingZoo library and the accompanying Agent Environment
Cycle (" AEC") games model. PettingZoo is a library of diverse sets of multi-agent …

Artificial-intelligence assistants and recommendation algorithms interact with billions of
people every day, influencing lives in myriad ways, yet they still have little understanding of …

Collaborating with humans requires rapidly adapting to their individual strengths,
weaknesses, and preferences. Unfortunately, most standard multi-agent reinforcement …