Zhaoan Wang1, Kishlay Jha2, Shaoping Xiao1,*
CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1319-1336, 2024, DOI:10.32604/cmc.2024.055809
- 15 October 2024
Abstract Climate change poses significant challenges to agricultural management, particularly in adapting to extreme weather conditions that impact agricultural production. Existing works with traditional Reinforcement Learning (RL) methods often falter under such extreme conditions. To address this challenge, our study introduces a novel approach by integrating Continual Learning (CL) with RL to form Continual Reinforcement Learning (CRL), enhancing the adaptability of agricultural management strategies. Leveraging the Gym-DSSAT simulation environment, our research enables RL agents to learn optimal fertilization strategies based on variable weather conditions. By incorporating CL algorithms, such as Elastic Weight Consolidation (EWC), with established… More >
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