BoreaRL: A Multi-Objective Reinforcement Learning Environment for Climate-Adaptive Boreal Forest Management
Dsouza, Kevin Bradley; Ofosu, Enoch; Amaogu, Daniel Chukwuemeka; Pigeon, Jérôme; Boudreault, Richard; Maghoul, Pooneh; Moreno-Cruz, Juan; Leonenko, Yuri · 2025-09-24
Introduces BoreaRL, a multi-objective reinforcement learning environment for climate-adaptive boreal forest management. Boreal forests store 30-40% of terrestrial carbon, much in climate-vulnerable permafrost soils. The authors show carbon sequestration goals are easier to optimize than permafrost preservation, and that effective policies must balance species composition and density to protect permafrost while maintaining carbon gains.
Dsouza, Kevin Bradley; Ofosu, Enoch; Amaogu, Daniel Chukwuemeka; Pigeon, Jérôme; Boudreault, Richard; Maghoul, Pooneh; Moreno-Cruz, Juan; Leonenko, Yuri. 2025-09-24. BoreaRL: A Multi-Objective Reinforcement Learning Environment for Climate-Adaptive Boreal Forest Management. https://arxiv.org/abs/2509.19846 (AI & Environment Resource Hub; record paper-453; collection snapshot 2026-09-15).
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