With the widespread adoption of deep learning, reinforcement learning (RL) has
experienced a dramatic increase in popularity, scaling to previously intractable problems …

Recent years have witnessed promising artificial intelligence (AI) applications in many
disciplines, including optics, engineering, medicine, economics, and education. In particular …

We present an autonomous chemical synthesis robot for the exploration, discovery, and
optimization of nanostructures driven by real-time spectroscopic feedback, theory, and …

Fine-tuning pretrained language models (LMs) without making any architectural changes
has become a norm for learning various language downstream tasks. However, for non …

The era of artificial neural network (ANN) began with a simplified application in many fields
and remarkable success in pattern recognition (PR) even in manufacturing industries …

This paper studies model-based reinforcement learning (RL) for regret minimization. We
focus on finite-horizon episodic RL where the transition model $ P $ belongs to a known …

Urban autonomous driving decision making is challenging due to complex road geometry
and multi-agent interactions. Current decision making methods are mostly manually …

This paper surveys the field of deep multiagent reinforcement learning (RL). The
combination of deep neural networks with RL has gained increased traction in recent years …

Abstract The notion of Cyber-Physical-Social System (CPSS) is an emerging concept
developed as a result of the need to understand the impact of Cyber-Physical Systems …

Quality diversity (QD) is a growing branch of stochastic optimization research that studies the
problem of generating an archive of solutions that maximize a given objective function but …