Multi-agent Reinforcement Learning (MARL) has gained wide attention in recent years and
has made progress in various fields. Specifically, cooperative MARL focuses on training a …

Learning to communicate efficiently is central to multi-agent reinforcement learning (MARL).
Existing methods often require agents to exchange messages intensively, which abuses …

This work uses game theory as a mathematical framework to address interaction modeling
in multi-agent motion forecasting and control. Despite its interpretability, applying game …

This paper presents the design, implementation, evaluation, and deployment of Meta's
production network migration system. We first introduce the network migration problem for …

Connected Autonomous Vehicles (CAVs) have attracted significant attention in recent years
and Reinforcement Learning (RL) has shown remarkable performance in improving the …

Deep cooperative multi-agent reinforcement learning has demonstrated its remarkable
success over a wide spectrum of complex control tasks. However, recent advances in multi …

Learning complex robot behavior through interactions with the environment necessitates
principled exploration. Effective strategies should prioritize exploring regions of the state …

Bayesian optimization (BO) is a powerful framework to optimize black-box expensive-to-
evaluate functions via sequential interactions. In several important problems (eg drug …

Exploring the process by which autonomous agents coordinate represents a pivotal
advancement toward emulating populations, which encompasses diverse applications in …

Several important real-world problems involve multiple entities interacting with each other
and can thus be modeled as multi-agent systems. Multi-agent systems are at the core of our …