Environmental variable regulation and optimisation strategy in forest seedling cultivation based on reinforcement learning

Key Points

• The central aim is to investigate how reinforcement learning can optimize environmental variables in forest seedling cultivation.
• Implemented reinforcement learning algorithms to analyze environmental variables
• Focused on optimizing conditions for seedling growth
• Evaluated the effectiveness of strategies through experimental setups
• Reinforcement learning successfully improved seedling growth under varied environmental conditions
• Identified key environmental factors that significantly enhance cultivation outcomes

Abstract