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

2025-08-26 · Mazyar Taghavi, Rahman Farnoosh arxiv

Protecting endangered wildlife from illegal poaching presents a critical challenge, particularly in vast and partially observable environments where real-time response is essential. This paper introduces a novel Expectation-Maximization (EM) based latent variable modeling approach in the context of Multi-Agent Reinforcement Learning (MARL) for Unmanned Aerial Vehicle (UAV) coordination in wildlife protection. By modeling hidden environmental factors and inter-agent dynamics through latent variables, our method enhances exploration and coordination under uncertainty.We implement and evaluate our EM-MARL framework using a custom simulation involving 10 UAVs tasked with patrolling protected habitats of the endangered Iranian leopard. Extensive experimental results demonstrate superior performance in detection accuracy, adaptability, and policy convergence when compared to standard algorithms such as Proximal Policy Optimization (PPO) and Deep Deterministic Policy Gradient (DDPG). Our findings underscore the potential of combining EM inference with MARL to improve decentralized decisionmaking in complex, high-stakes conservation scenarios. The full implementation, simulation environment, and training scripts are publicly available on GitHub.

📄 PDF Abstract BibTeX arXiv:2509.02579

Code (0)

등록된 구현이 없습니다.

Tasks

Multi-agent Reinforcement Learning

MABL: Bi-Level Latent-Variable World Model for Sample-Efficient Multi-Agent Reinforcement Learning

2023-04-12 · Aravind Venugopal, Stephanie Milani, Fei Fang, Balaraman Ravindran

Multi-agent reinforcement learning (MARL) methods often suffer from high sample complexity, limiting their use in real-world problems where data is sparse or expensive to collect. Although latent-variable world models ha…

Multi-agent Reinforcement Learning reinforcement-learning SMAC SMAC+

Diffusion for World Modeling: Visual Details Matter in Atari

2024-05-20 · Eloi Alonso, Adam Jelley, Vincent Micheli, Anssi Kanervisto 외

World models constitute a promising approach for training reinforcement learning agents in a safe and sample-efficient manner. Recent world models predominantly operate on sequences of discrete latent variables to model …

Image Generation reinforcement-learning Reinforcement Learning

Rethinking Action Spaces for Reinforcement Learning in End-to-end Dialog Agents with Latent Variable Models

2019-02-23 · NAACL 2019 6 · Tiancheng Zhao, Kaige Xie, Maxine Eskenazi

Defining action spaces for conversational agents and optimizing their decision-making process with reinforcement learning is an enduring challenge. Common practice has been to use handcrafted dialog acts, or the output v…

Decision Making Dialogue Generation Dialogue Management Goal-Oriented Dialogue Systems+3

Hierarchical Cooperative Multi-Agent Reinforcement Learning with Skill Discovery

2019-12-07 · Jiachen Yang, Igor Borovikov, Hongyuan Zha

Human players in professional team sports achieve high level coordination by dynamically choosing complementary skills and executing primitive actions to perform these skills. As a step toward creating intelligent agents…

Multi-agent Reinforcement Learning Q-Learning reinforcement-learning Reinforcement Learning+1

Modeling the Long Term Future in Model-Based Reinforcement Learning

2019-05-01 · ICLR 2019 5 · Nan Rosemary Ke, Amanpreet Singh, Ahmed Touati, Anirudh Goyal 외

In model-based reinforcement learning, the agent interleaves between model learning and planning. These two components are inextricably intertwined. If the model is not able to provide sensible long-term prediction, th…

Imitation Learning Model-based Reinforcement Learning reinforcement-learning Reinforcement Learning+3