Reinforcement learning-based adaptive strategies for climate change adaptation: An application for coastal flood risk management.

ABSTRACT: Conventional computational models of climate adaptation frameworks inadequately consider decision-makers' capacity to learn, update, and improve decisions. Here, we investigate the potential of reinforcement learning (RL), a machine learning technique that efficaciously acquires knowledge from the environment and systematically optimizes dynamic decisions, in modeling and informing adaptive climate decision-making. We consider coastal flood risk mitigations for Manhattan, New York City, USA (NYC), illustrating the benefit of continuously incorporating observations of sea-level rise into systematic designs of adaptive strategies. We find that when designing adaptive seawalls to protect NYC, the RL-derived strategy significantly reduces the expected net cost by 6 to 36% under the moderate em

SUBMITTER: Feng K 

PROVIDER: S-EPMC11962498 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature