DCHAS: Dual-Channel Heterogeneous Agent System for Optimizing Agricultural Irrigation Decisions
Buwen Liu, Liruizhi Jia, Shengquan Liu, Bo Kong, Xiaoli Zhang, Guangxin Yu · 2025
This paper proposes a Dual-Channel Heterogeneous Agent System (DCHAS) for optimizing agricultural irrigation decisions. The irrigation decision problem is formulated as a fully cooperative Decentralized Partially Observable Markov Decision Process (DEC-POMDP), and two heterogeneous agent channels are designed: one channel focuses on historical environmental information to determine irrigation timing, while the other channel uses real-time crop growth status to determine irrigation volume. By sharing key data, the two agent channels work collaboratively to ensure the consistency and coordination of irrigation strategies. To further enhance system performance, a globally shared reward function is designed to incorporate the multi-objective optimization of crop yield and water consumption within a reinforcement learning framework, thereby promoting cooperative interaction between the agent channels. Experimental results show that, compared to baseline models, DCHAS demonstrates higher flexibility and adaptability under complex environmental and resource-constrained conditions, while also reducing training time and accelerating convergence.