Publication

Abstract : This paper focuses on developing a comprehensive system for optimised climate control in a simulated greenhouse setting with complex environment modelling, communication-enabled agents, and hyperparameter optimisation using multiagent reinforcement learning (MARL). The greenhouse environment model gives the agents a richer and more detailed state representation by extending pertinent climate variables, plant health indicators, and resource levels for each zone. In order to stimulate both individual zone performance and overall greenhouse goals, the incentive function has been changed. This involves goals like preserving a stable climate, optimising resource use, or achieving a balanced yield. Based on the communication-based MARL strategy used, information sharing between agents is made possible by the communication protocols included into multi-agent architecture. This enables the agents to work together and make decisions in unison. Neural network designs are used to build both value and policy networks, allowing the agents to understand intricate relationships and decide on the best course of action depending on the observed situation.