Zobrazeno 1 - 10
of 52
pro vyhledávání: '"Zhou, Hanhan"'
Autor:
Chen, Jingdi, Zhou, Hanhan, Mei, Yongsheng, Joe-Wong, Carlee, Adam, Gina, Bastian, Nathaniel D., Lan, Tian
Deep Reinforcement Learning (DRL) algorithms have achieved great success in solving many challenging tasks while their black-box nature hinders interpretability and real-world applicability, making it difficult for human experts to interpret and unde
Externí odkaz:
http://arxiv.org/abs/2410.16517
With the advancements of artificial intelligence (AI), we're seeing more scenarios that require AI to work closely with other agents, whose goals and strategies might not be known beforehand. However, existing approaches for training collaborative ag
Externí odkaz:
http://arxiv.org/abs/2403.15341
Publikováno v:
AAAI 2024
Value function factorization has achieved great success in multi-agent reinforcement learning by optimizing joint action-value functions through the maximization of factorized per-agent utilities. To ensure Individual-Global-Maximum property, existin
Externí odkaz:
http://arxiv.org/abs/2312.15555
Many cybersecurity problems that require real-time decision-making based on temporal observations can be abstracted as a sequence modeling problem, e.g., network intrusion detection from a sequence of arriving packets. Existing approaches like reinfo
Externí odkaz:
http://arxiv.org/abs/2312.07696
Cross-device Federated Learning (FL) faces significant challenges where low-end clients that could potentially make unique contributions are excluded from training large models due to their resource bottlenecks. Recent research efforts have focused o
Externí odkaz:
http://arxiv.org/abs/2310.08670
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 work has shown that offline reinforcement learning can be formulate
Externí odkaz:
http://arxiv.org/abs/2308.14897
Autor:
Chen, Chang-Lin, Zhou, Hanhan, Chen, Jiayu, Pedramfar, Mohammad, Lan, Tian, Zhu, Zheqing, Zhou, Chi, Ruiz, Pol Mauri, Kumar, Neeraj, Dong, Hongbo, Aggarwal, Vaneet
Online optimization of resource management for large-scale data centers and infrastructures to meet dynamic capacity reservation demands and various practical constraints (e.g., feasibility and robustness) is a very challenging problem. Mixed Integer
Externí odkaz:
http://arxiv.org/abs/2306.17054
Experience replay is crucial for off-policy reinforcement learning (RL) methods. By remembering and reusing the experiences from past different policies, experience replay significantly improves the training efficiency and stability of RL algorithms.
Externí odkaz:
http://arxiv.org/abs/2302.10418
Value function factorization methods have become a dominant approach for cooperative multiagent reinforcement learning under a centralized training and decentralized execution paradigm. By factorizing the optimal joint action-value function using a m
Externí odkaz:
http://arxiv.org/abs/2302.05593
Publikováno v:
Advances in Neural Information Processing Systems 35 (2022): 15757-15769
Multi-agent reinforcement learning (MARL) has witnessed significant progress with the development of value function factorization methods. It allows optimizing a joint action-value function through the maximization of factorized per-agent utilities d
Externí odkaz:
http://arxiv.org/abs/2206.11420