Probabilistic Ecosystems Assessment with Reinforcement Learning.

The health of marine ecosystems is crucial for planetary well-being, yet monitoring these dynamic and often remote environments remains a pressing challenge, further exacerbated by the impacts of climate change. Rising ocean temperatures, acidification, and habitat degradation are altering biodiversity distributions, increasing the need for rapid advancements in monitoring techniques. While current data collection techniques yield valuable ecological insights, they are frequently hindered by logistical constraints, high operational costs, and limited spatiotemporal coverage, underscoring the need for innovative, interdisciplinary approaches. The PEARL framework addresses this gap by integrating probabilistic ecosystems assessment and reinforcement learning (RL) to develop adaptive, explainable tools for biodiversity monitoring in the context of autonomous underwater vehicles (AUVs). At the core of PEARL lies HexaWorld, a reinforcement learning environment structured on hexagonal tiling, designed to optimize AUV navigation in complex marine landscapes. By incorporating partial observability and a multi-objective reward function, HexaWorld enables AUVs to strategically explore biodiversity hotspots while efficiently balancing energy consumption and travel distance, mitigating the inefficiencies inherent in traditional grid-based approaches. Complementing this exploration strategy, Generalized Mixture Models (GMMs), incorporating both positive and negative weights, leverage environmental covariates collected during navigation to model biodiversity distributions. Unlike their positive-weighted counterparts, these models offer a more compact and accurate representation of complex, multimodal patterns, improving the identification of biodiversity hotspots and their relationship with underlying environmental drivers. By embedding GMMs within the RL framework, PEARL translates optimal exploration policies into explainable ecological insights, enhancing the transparency of data-driven decisions and supporting more informed conservation and restoration strategies.

Grantor dc:publisher

Università degli studi di Trento

Year dc:date

2025

Author dc:creator

• Lombardi, Giulia

Contributors dc:contributor

• Bianchi, Luigi Amedeo

• Reinforcement Learning
• Explainable AI
• Generalized Mixture Models

Statement dc:rights

• info:eu-repo/semantics/openAccess
• license:Creative commons
• license uri:http://creativecommons.org/licenses/by-nc-nd/4.0/

Language dc:language

eng

Identifier

http://dx.doi.org/10.15168/11572_459561
10.15168/11572_459561

OAI identifier oai:identifier

oai:iris.unitn.it:11572/459561