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

We study the problem of training a Reinforcement Learning (RL) agent that is collaborative
with humans without using human data. Although such agents can be obtained through self …

There is a recent trend of applying multi-agent reinforcement learning (MARL) to train an
agent that can cooperate with humans in a zero-shot fashion without using any human data …

Zero-shot coordination in cooperative artificial intelligence (AI) remains a significant
challenge, which means effectively coordinating with a wide range of unseen partners …

The difficulty of appropriately assigning credit is particularly heightened in cooperative
MARL with sparse reward, due to the concurrent time and structural scales involved …

Securing coordination between AI agent and teammates (human players or AI agents) in
contexts involving unfamiliar humans continues to pose a significant challenge in Zero-Shot …

Generalization is a major challenge for multi-agent reinforcement learning. How well does
an agent perform when placed in novel environments and in interactions with new co …

Models of human behavior for prediction and collaboration tend to fall into two categories:
ones that learn from large amounts of data via imitation learning, and ones that assume …

Abstract Knowledge and skills can transfer from human teachers to human students.
However, such direct transfer is often not scalable for physical tasks, as they require one-to …

Human-AI shared control allows human to interact and collaborate with autonomous agents
to accomplish control tasks in complex environments. Previous Reinforcement Learning …