Recent years have seen an increasing interest in Demand Response (DR) as a means to
provide flexibility, and hence improve the reliability of energy systems in a cost-effective way …

Recent years have witnessed significant advances in reinforcement learning (RL), which
has registered tremendous success in solving various sequential decision-making problems …

This work identifies 18 foundational challenges in assuring the alignment and safety of large
language models (LLMs). These challenges are organized into three different categories …

The advances in reinforcement learning have recorded sublime success in various domains.
Although the multi-agent domain has been overshadowed by its single-agent counterpart …

To promote better performance-bandwidth trade-off for multi-agent perception, we propose a
novel distilled collaboration graph (DiscoGraph) to model trainable, pose-aware, and …

Communication is an effective mechanism for coordinating the behaviors of multiple agents,
broadening their views of the environment, and to support their collaborations. In the field of …

Reinforcement learning (RL) has become a highly successful framework for learning in
Markov decision processes (MDP). Due to the adoption of RL in realistic and complex …

Following the remarkable success of the AlphaGO series, 2019 was a booming year that
witnessed significant advances in multi-agent reinforcement learning (MARL) techniques …

Agent-based modeling (ABM) is a novel computational methodology for representing the
behavior of individuals in order to study social phenomena. Its use is rapidly growing in …

Many real-world problems, such as network packet routing and the coordination of
autonomous vehicles, are naturally modelled as cooperative multi-agent systems. There is a …