Bridging adaptive management and reinforcement learning for more robust decisions

Area of Science:

• Artificial Intelligence
• Environmental Management
• Biodiversity Science

Background:

• Artificial intelligence (AI) methods excel at complex decision-making in uncertain environments.
• Adaptive environmental management seeks to improve strategies through experience and updated knowledge.

Purpose of the Study:

• To explore the application of reinforcement learning (RL), an AI subfield, to environmental management.
• To assess RL's potential for improving evidence-informed adaptive management decisions under uncertainty.

Main Methods:

• Review of reinforcement learning principles and their parallels with adaptive environmental management.
• Analysis of RL's applicability when classical optimization methods are intractable.
• Discussion of technical and social challenges in applying RL to environmental systems.

Main Results:

• Reinforcement learning (RL) offers a promising framework for adaptive environmental management.
• RL can enhance decision-making in high-dimensional, uncertain environmental systems.
• Identified technical and social considerations for RL implementation in this domain.

Conclusions:

• Environmental management and computer science can mutually benefit from understanding experience-based decision-making.
• RL provides a valuable lens for improving strategies in adaptive environmental management.
• Further interdisciplinary collaboration is encouraged to leverage AI for environmental challenges.