Reinforcement Learning (RL) has achieved tremendous success in many complex decision-
making tasks. However, safety concerns are raised during deploying RL in real-world …

As materials researchers increasingly embrace machine-learning (ML) methods, it is natural
to wonder what lessons can be learned from other fields undergoing similar developments …

Believable proxies of human behavior can empower interactive applications ranging from
immersive environments to rehearsal spaces for interpersonal communication to prototyping …

First-person view (FPV) drone racing is a televised sport in which professional competitors
pilot high-speed aircraft through a 3D circuit. Each pilot sees the environment from 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 …

Pretraining on noisy, internet-scale datasets has been heavily studied as a technique for
training models with broad, general capabilities for text, images, and other modalities …

Metaverse as the latest buzzword has attracted great attention from both industry and
academia. Metaverse seamlessly integrates the real world with the virtual world and allows …

Many potential applications of artificial intelligence involve making real-time decisions in
physical systems while interacting with humans. Automobile racing represents an extreme …

Deep reinforcement learning (RL) algorithms are predominantly evaluated by comparing
their relative performance on a large suite of tasks. Most published results on deep RL …

We introduce DeepNash, an autonomous agent that plays the imperfect information game
Stratego at a human expert level. Stratego is one of the few iconic board games that artificial …