Multi-agent reinforcement learning: Methods, applications, visionary prospects, and challenges
Multi-agent reinforcement learning (MARL) is a widely used Artificial Intelligence (AI)
technique. However, current studies and applications need to address its scalability, non …
technique. However, current studies and applications need to address its scalability, non …
Solving a class of non-convex minimax optimization in federated learning
The minimax problems arise throughout machine learning applications, ranging from
adversarial training and policy evaluation in reinforcement learning to AUROC …
adversarial training and policy evaluation in reinforcement learning to AUROC …
A survey of progress on cooperative multi-agent reinforcement learning in open environment
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 …
has made progress in various fields. Specifically, cooperative MARL focuses on training a …
Robust multi-agent reinforcement learning via adversarial regularization: Theoretical foundation and stable algorithms
Abstract Multi-Agent Reinforcement Learning (MARL) has shown promising results across
several domains. Despite this promise, MARL policies often lack robustness and are …
several domains. Despite this promise, MARL policies often lack robustness and are …
Serverless federated auprc optimization for multi-party collaborative imbalanced data mining
To address the big data challenges, serverless multi-party collaborative training has recently
attracted attention in the data mining community, since they can cut down the …
attracted attention in the data mining community, since they can cut down the …
What is the Solution for State-Adversarial Multi-Agent Reinforcement Learning?
Various methods for Multi-Agent Reinforcement Learning (MARL) have been developed
with the assumption that agents' policies are based on accurate state information. However …
with the assumption that agents' policies are based on accurate state information. However …
Robust electric vehicle balancing of autonomous mobility-on-demand system: A multi-agent reinforcement learning approach
Electric autonomous vehicles (EAVs) are getting attention in future autonomous mobility-on-
demand (AMoD) systems due to their economic and societal benefits. However, EAVs' …
demand (AMoD) systems due to their economic and societal benefits. However, EAVs' …
Collaborative multi-object tracking with conformal uncertainty propagation
Object detection and multiple object tracking (MOT) are essential components of self-driving
systems. Accurate detection and uncertainty quantification are both critical for onboard …
systems. Accurate detection and uncertainty quantification are both critical for onboard …
Sample-Efficient Robust Multi-Agent Reinforcement Learning in the Face of Environmental Uncertainty
To overcome the sim-to-real gap in reinforcement learning (RL), learned policies must
maintain robustness against environmental uncertainties. While robust RL has been widely …
maintain robustness against environmental uncertainties. While robust RL has been widely …
Statistically efficient variance reduction with double policy estimation for off-policy evaluation in sequence-modeled reinforcement learning
H Zhou, T Lan, V Aggarwal - arXiv preprint arXiv:2308.14897, 2023 - arxiv.org
Offline reinforcement learning aims to utilize datasets of previously gathered environment-
action interaction records to learn a policy without access to the real environment. Recent …
action interaction records to learn a policy without access to the real environment. Recent …