Zobrazeno 1 - 10
of 91
pro vyhledávání: '"WANG Tonghan"'
Multiagent systems grapple with partial observability (PO), and the decentralized POMDP (Dec-POMDP) model highlights the fundamental nature of this challenge. Whereas recent approaches to address PO have appealed to deep learning models, providing a
Externí odkaz:
http://arxiv.org/abs/2410.13953
Applying Reinforcement Learning (RL) to Restless Multi-Arm Bandits (RMABs) offers a promising avenue for addressing allocation problems with resource constraints and temporal dynamics. However, classic RMAB models largely overlook the challenges of (
Externí odkaz:
http://arxiv.org/abs/2408.05686
The increasing deployment of AI is shaping the future landscape of the internet, which is set to become an integrated ecosystem of AI agents. Orchestrating the interaction among AI agents necessitates decentralized, self-sustaining mechanisms that ha
Externí odkaz:
http://arxiv.org/abs/2407.18074
Automated mechanism design (AMD) uses computational methods for mechanism design. Differentiable economics is a form of AMD that uses deep learning to learn mechanism designs and has enabled strong progress in AMD in recent years. Nevertheless, a maj
Externí odkaz:
http://arxiv.org/abs/2406.07428
Autor:
Zhang, Edwin, Zhao, Sadie, Wang, Tonghan, Hossain, Safwan, Gasztowtt, Henry, Zheng, Stephan, Parkes, David C., Tambe, Milind, Chen, Yiling
Artificial Intelligence (AI) holds promise as a technology that can be used to improve government and economic policy-making. This paper proposes a new research agenda towards this end by introducing Social Environment Design, a general framework for
Externí odkaz:
http://arxiv.org/abs/2402.14090
We consider the multi-sender persuasion problem: multiple players with informational advantage signal to convince a single self-interested actor to take certain actions. This problem generalizes the seminal Bayesian Persuasion framework and is ubiqui
Externí odkaz:
http://arxiv.org/abs/2402.04971
In the realm of multi-agent reinforcement learning, intrinsic motivations have emerged as a pivotal tool for exploration. While the computation of many intrinsic rewards relies on estimating variational posteriors using neural network approximators,
Externí odkaz:
http://arxiv.org/abs/2308.09909
Publikováno v:
NeurIPS 2023
Contract design involves a principal who establishes contractual agreements about payments for outcomes that arise from the actions of an agent. In this paper, we initiate the study of deep learning for the automated design of optimal contracts. We i
Externí odkaz:
http://arxiv.org/abs/2307.02318
Robot design aims at learning to create robots that can be easily controlled and perform tasks efficiently. Previous works on robot design have proven its ability to generate robots for various tasks. However, these works searched the robots directly
Externí odkaz:
http://arxiv.org/abs/2306.00036
Publikováno v:
NeurIPS 2022
Value decomposition multi-agent reinforcement learning methods learn the global value function as a mixing of each agent's individual utility functions. Coordination graphs (CGs) represent a higher-order decomposition by incorporating pairwise payoff
Externí odkaz:
http://arxiv.org/abs/2211.08404