Copertina

Latent Variable Modeling in Multi-Agent Reinforcement Learning via Expectation-Maximization for UAV-Based Wildlife Protection

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Taghavi, Mazyar, Farnoosh, Rahman
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Machine Learning Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Quantum Computing and Neuromorphic Computing for Safe, Reliable, and explainable Multi-Agent Reinforcement Learning: Optimal Control in Autonomous Robotics
di: Taghavi, Mazyar, et al.
Pubblicazione: (2024)

Application of Unsupervised Artificial Neural Network (ANN) Self_Organizing Map (SOM) in Identifying Main Car Sales Factors
di: Taghavi, Mazyar
Pubblicazione: (2024)

Quantum-Inspired Multi Agent Reinforcement Learning for Exploration Exploitation Optimization in UAV-Assisted 6G Network Deployment
di: Taghavi, Mazyar, et al.
Pubblicazione: (2025)

An Expectation-Maximization Algorithm for Domain Adaptation in Gaussian Causal Models
di: Javidian, Mohammad Ali
Pubblicazione: (2026)

UAV-MARL: Multi-Agent Reinforcement Learning for Time-Critical and Dynamic Medical Supply Delivery
di: Guven, Islam, et al.
Pubblicazione: (2026)

Non-myopic Matching and Rebalancing in Large-Scale On-Demand Ride-Pooling Systems Using Simulation-Informed Reinforcement Learning
di: Namdarpour, Farnoosh, et al.
Pubblicazione: (2025)

Revealing the Challenges of Sim-to-Real Transfer in Model-Based Reinforcement Learning via Latent Space Modeling
di: Lin, Zhilin, et al.
Pubblicazione: (2025)

Bayesian Deep Learning Via Expectation Maximization and Turbo Deep Approximate Message Passing
di: Xu, Wei, et al.
Pubblicazione: (2024)

Constrained Latent Action Policies for Model-Based Offline Reinforcement Learning
di: Alles, Marvin, et al.
Pubblicazione: (2024)

DiffEM: Learning from Corrupted Data with Diffusion Models via Expectation Maximization
di: Hosseintabar, Danial, et al.
Pubblicazione: (2025)

HiVAE: Hierarchical Latent Variables for Scalable Theory of Mind
di: Doering, Nigel, et al.
Pubblicazione: (2026)

To Code or not to Code? Adaptive Tool Integration for Math Language Models via Expectation-Maximization
di: Wang, Haozhe, et al.
Pubblicazione: (2025)

Reinforcement Learning with Conditional Expectation Reward
di: Xiao, Changyi, et al.
Pubblicazione: (2026)

Detecting Training Data of Large Language Models via Expectation Maximization
di: Kim, Gyuwan, et al.
Pubblicazione: (2024)

Bridge the Inference Gaps of Neural Processes via Expectation Maximization
di: Wang, Qi, et al.
Pubblicazione: (2025)

Expectation Maximization Pseudo Labels
di: Xu, Moucheng, et al.
Pubblicazione: (2023)

GEPO: Group Expectation Policy Optimization for Stable Heterogeneous Reinforcement Learning
di: Zhang, Han, et al.
Pubblicazione: (2025)

Age and Power Minimization via Meta-Deep Reinforcement Learning in UAV Networks
di: Sarathchandra, Sankani, et al.
Pubblicazione: (2025)

Partial Models for Building Adaptive Model-Based Reinforcement Learning Agents
di: Alver, Safa, et al.
Pubblicazione: (2024)

Enabling Multi-Agent Transfer Reinforcement Learning via Scenario Independent Representation
di: Nipu, Ayesha Siddika, et al.
Pubblicazione: (2024)

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

Multi-Scenario Combination Based on Multi-Agent Reinforcement Learning to Optimize the Advertising Recommendation System
di: Zhao, Yang, et al.
Pubblicazione: (2024)

Learning Evolving Latent Strategies for Multi-Agent Language Systems without Model Fine-Tuning
di: Tang, Wenlong
Pubblicazione: (2025)

Fair In-Context Learning via Latent Concept Variables
di: Bhaila, Karuna, et al.
Pubblicazione: (2024)

Scalable Random Feature Latent Variable Models
di: Li, Ying, et al.
Pubblicazione: (2024)

FDRMFL:Multi-modal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning
di: Wu, Haozhe
Pubblicazione: (2025)

Multi-objective Reinforcement Learning with Nonlinear Preferences: Provable Approximation for Maximizing Expected Scalarized Return
di: Peng, Nianli, et al.
Pubblicazione: (2023)

Local Causal Structure Learning in the Presence of Latent Variables
di: Xie, Feng, et al.
Pubblicazione: (2024)

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

KARL: Knowledge Agents via Reinforcement Learning
di: Chang, Jonathan D., et al.
Pubblicazione: (2026)

Towards Principled Unsupervised Multi-Agent Reinforcement Learning
di: Zamboni, Riccardo, et al.
Pubblicazione: (2025)

medDreamer: Model-Based Reinforcement Learning with Latent Imagination on Complex EHRs for Clinical Decision Support
di: Xu, Qianyi, et al.
Pubblicazione: (2025)

$λ$-models: Effective Decision-Aware Reinforcement Learning with Latent Models
di: Voelcker, Claas A, et al.
Pubblicazione: (2023)

MIR: Efficient Exploration in Episodic Multi-Agent Reinforcement Learning via Mutual Intrinsic Reward
di: Chen, Kesheng, et al.
Pubblicazione: (2025)

Comprehend, Divide, and Conquer: Feature Subspace Exploration via Multi-Agent Hierarchical Reinforcement Learning
di: Zhang, Weiliang, et al.
Pubblicazione: (2025)

Scalable Constrained Multi-Agent Reinforcement Learning via State Augmentation and Consensus for Separable Dynamics
di: Amaya-Corredor, Santiago, et al.
Pubblicazione: (2026)

A Model-Based Solution to the Offline Multi-Agent Reinforcement Learning Coordination Problem
di: Barde, Paul, et al.
Pubblicazione: (2023)

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

PDRL: Multi-Agent based Reinforcement Learning for Predictive Monitoring
di: Shaik, Thanveer, et al.
Pubblicazione: (2023)

Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning
di: Zhu, Ting, et al.
Pubblicazione: (2024)