Copertina

Explaining Decentralized Multi-Agent Reinforcement Learning Policies

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Boggess, Kayla, Kraus, Sarit, Feng, Lu
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Explaining Decisions of Agents in Mixed-Motive Games
di: Orner, Maayan, et al.
Pubblicazione: (2024)

ADESSE: Advice Explanations in Complex Repeated Decision-Making Environments
di: Schleibaum, Sören, et al.
Pubblicazione: (2024)

Bayesian Persuasion with Externalities: Exploiting Agent Types
di: Shaki, Jonathan, et al.
Pubblicazione: (2024)

Evaluating Human Trust in LLM-Based Planners: A Preliminary Study
di: Chen, Shenghui, et al.
Pubblicazione: (2025)

Robust Solutions for Multi-Defender Stackelberg Security Games
di: Mutzari, Dolev, et al.
Pubblicazione: (2022)

The Persuasion Paradox: When LLM Explanations Fail to Improve Human-AI Team Performance
di: Cohen, Ruth, et al.
Pubblicazione: (2026)

Contrastive Explanations of Centralized Multi-agent Optimization Solutions
di: Zehtabi, Parisa, et al.
Pubblicazione: (2023)

Fully Decentralized Cooperative Multi-Agent Reinforcement Learning: A Survey
di: Jiang, Jiechuan, et al.
Pubblicazione: (2024)

Strategic Communication under Threat: Learning Information Trade-offs in Pursuit-Evasion Games
di: La Gatta, Valerio, et al.
Pubblicazione: (2025)

Cognitive Effects in Large Language Models
di: Shaki, Jonathan, et al.
Pubblicazione: (2023)

Optimistic Gradient Learning with Hessian Corrections for High-Dimensional Black-Box Optimization
di: Kfir, Yedidya, et al.
Pubblicazione: (2025)

Integrating Policy Summaries with Reward Decomposition for Explaining Reinforcement Learning Agents
di: Septon, Yael, et al.
Pubblicazione: (2022)

Voter Priming Campaigns: Strategies, Equilibria, and Algorithms
di: Shaki, Jonathan, et al.
Pubblicazione: (2024)

Design a Win-Win Strategy That Is Fair to Both Service Providers and Tasks When Rejection Is Not an Option
di: Trabelsi, Yohai, et al.
Pubblicazione: (2024)

Goal-Oriented Multi-Agent Reinforcement Learning for Decentralized Agent Teams
di: Du, Hung, et al.
Pubblicazione: (2025)

Learning Nonlinear Causal Reductions to Explain Reinforcement Learning Policies
di: Kekić, Armin, et al.
Pubblicazione: (2025)

Hide-and-Shill: A Reinforcement Learning Framework for Market Manipulation Detection in Symphony-a Decentralized Multi-Agent System
di: Shi, Ronghua, et al.
Pubblicazione: (2025)

Reward-Independent Messaging for Decentralized Multi-Agent Reinforcement Learning
di: Yoshida, Naoto, et al.
Pubblicazione: (2025)

Decentralizing Multi-Agent Reinforcement Learning with Temporal Causal Information
di: Corazza, Jan, et al.
Pubblicazione: (2025)

Contextual Knowledge Sharing in Multi-Agent Reinforcement Learning with Decentralized Communication and Coordination
di: Du, Hung, et al.
Pubblicazione: (2025)

Measuring Policy Distance for Multi-Agent Reinforcement Learning
di: Hu, Tianyi, et al.
Pubblicazione: (2024)

TAG: A Decentralized Framework for Multi-Agent Hierarchical Reinforcement Learning
di: Paolo, Giuseppe, et al.
Pubblicazione: (2025)

Explaining Strategic Decisions in Multi-Agent Reinforcement Learning for Aerial Combat Tactics
di: Selmonaj, Ardian, et al.
Pubblicazione: (2025)

Mxplainer: Explain and Learn Insights by Imitating Mahjong Agents
di: Li, Lingfeng, et al.
Pubblicazione: (2025)

Multi-Agent Reinforcement Learning Simulation for Environmental Policy Synthesis
di: Rudd-Jones, James, et al.
Pubblicazione: (2025)

$K$-Level Policy Gradients for Multi-Agent Reinforcement Learning
di: Reddi, Aryaman, et al.
Pubblicazione: (2025)

Multi-Agent Reinforcement Learning Meets Leaf Sequencing in Radiotherapy
di: Gao, Riqiang, et al.
Pubblicazione: (2024)

A Survey of Multi Agent Reinforcement Learning: Federated Learning and Cooperative and Noncooperative Decentralized Regimes
di: Cheruiyot, Kemboi, et al.
Pubblicazione: (2025)

LAMARL: LLM-Aided Multi-Agent Reinforcement Learning for Cooperative Policy Generation
di: Zhu, Guobin, et al.
Pubblicazione: (2025)

Centralized vs. Decentralized Multi-Agent Reinforcement Learning for Enhanced Control of Electric Vehicle Charging Networks
di: Shojaeighadikolaei, Amin, et al.
Pubblicazione: (2024)

Facilitating Matches on Allocation Platforms
di: Trabelsi, Yohai, et al.
Pubblicazione: (2025)

Pro-AI Bias in Large Language Models
di: Trabelsi, Benaya, et al.
Pubblicazione: (2026)

PRISMA: Reinforcement Learning Guided Two-Stage Policy Optimization in Multi-Agent Architecture for Open-Domain Multi-Hop Question Answering
di: Liu, Yu, et al.
Pubblicazione: (2026)

Offline Multi-Agent Reinforcement Learning via In-Sample Sequential Policy Optimization
di: Liu, Zongkai, et al.
Pubblicazione: (2024)

Off-Policy Correction For Multi-Agent Reinforcement Learning
di: Zawalski, Michał, et al.
Pubblicazione: (2021)

Agent-Based Decentralized Energy Management of EV Charging Station with Solar Photovoltaics via Multi-Agent Reinforcement Learning
di: Fan, Jiarong, et al.
Pubblicazione: (2025)

Centralized Permutation Equivariant Policy for Cooperative Multi-Agent Reinforcement Learning
di: Xu, Zhuofan, et al.
Pubblicazione: (2025)

The Complexity of Manipulation of k-Coalitional Games on Graphs
di: Barr, Hodaya, et al.
Pubblicazione: (2024)

Vision-Based Generic Potential Function for Policy Alignment in Multi-Agent Reinforcement Learning
di: Ma, Hao, et al.
Pubblicazione: (2025)

Concept Learning for Cooperative Multi-Agent Reinforcement Learning
di: Ge, Zhonghan, et al.
Pubblicazione: (2025)