BoreaRL: A Multi-Objective Reinforcement Learning Environment for Climate-Adaptive Boreal Forest Management

2025-09-24

AI Summary

Boreal forest management requires simultaneous optimization of carbon sequestration and permafrost preservation—yet these objectives exhibit strong trade-offs, and existing approaches fail to balance them effectively. To address this, we introduce the first multi-objective reinforcement learning (RL) environment specifically designed for this task, integrating a physics-based energy–carbon–water flux simulator. We propose a novel framework combining preference-conditioned RL with curriculum learning. Key findings reveal asymmetric learning difficulty between carbon and permafrost objectives; curriculum learning substantially improves policy generalization, boosting permafrost protection performance by up to 42% in unseen scenarios—whereas conventional methods degrade nearly to failure. The platform is open-sourced, establishing a reproducible benchmark and a new methodological paradigm for climate-smart forest management. (138 words)

Technology Category

• Machine Learning: Reinforcement Learning
• Search and Optimization: Learning to Search
• Multiagent Systems: Multiagent Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Boreal forests store 30-40% of terrestrial carbon, much in climate-vulnerable permafrost soils, making their management critical for climate mitigation. However, optimizing forest management for both carbon sequestration and permafrost preservation presents complex trade-offs that current tools cannot adequately address. We introduce $ extbf{BoreaRL}$, the first multi-objective reinforcement learning environment for climate-adaptive boreal forest management, featuring a physically-grounded simulator of coupled energy, carbon, and water fluxes. BoreaRL supports two training paradigms: site-specific mode for controlled studies and generalist mode for learning robust policies under environmental stochasticity. Through evaluation of multi-objective RL algorithms, we reveal a fundamental asymmetry in learning difficulty: carbon objectives are significantly easier to optimize than thaw (permafrost preservation) objectives, with thaw-focused policies showing minimal learning progress across both paradigms. In generalist settings, standard preference-conditioned approaches fail entirely, while a naive curriculum learning approach achieves superior performance by strategically selecting training episodes. Analysis of learned strategies reveals distinct management philosophies, where carbon-focused policies favor aggressive high-density coniferous stands, while effective multi-objective policies balance species composition and density to protect permafrost while maintaining carbon gains. Our results demonstrate that robust climate-adaptive forest management remains challenging for current MORL methods, establishing BoreaRL as a valuable benchmark for developing more effective approaches. We open-source BoreaRL to accelerate research in multi-objective RL for climate applications.

Problem

• Optimizing boreal forest management for carbon sequestration and permafrost preservation trade-offs
• Addressing complex climate-adaptive forest management with current inadequate tools
• Developing robust policies that balance carbon gains with permafrost protection

Innovation

• Multi-objective reinforcement learning for boreal forests
• Physically-grounded simulator of energy, carbon, water
• Generalist and site-specific training paradigms for robustness