Multi-Agent Coordination via Policy Gradient
Exploring Collaborative Intelligence with PPO Algorithms
Project Overview
Multi-Agent PPO
This research delves into Proximal Policy Optimization (PPO) and its multi-agent adaptation (MAPPO) within a simulated football environment. I analyze the mechanics of cooperation and decision-making in multi-agent systems, exploring how policy gradient methods facilitate coordinated actions in a high-dimensional, partially observable setting. This study contributes to a deeper understanding of collective AI behaviors, relevant to autonomous systems and robotics.
His expertise in ML is strong, Kevin is always ready to illuminate even the most tangled concepts. Kevin's dedication and clear explanations propelled our team across the finish line, leaving us all immensely grateful for his contribution.
— Jared Benedict
Kevin is not only a great, knowledgeable teammate, but also an exceptional technical leader, who always ensured our milestones were met. Given the opportunity, I would happily work with Kevin again.
— Jarrod Pelley
His commitment to excellence is truly commendable; he approached every challenge with a determined mindset, setting a high standard for the entire team. Kevin's collaborative spirit made him an invaluable team player. He seamlessly integrated with our group, fostering a positive and productive atmosphere.
— Vipul Koti
Excuse me for using a sports metaphor but Kevin was like Michael Jordan to our dream team. He has an incredible grasp for ML and a good understanding of what it takes to succeed in the field. His unique ability to communicate and his commitment and determination inspired us to do our best work.
— Clivens LaGuerre
He consistently showcased leadership skills, building out a plan for our project, working with team members to utilize their skills, and making sure that everything on our project roadmap was completed and successful.
— Eric Nagel