In simulations of greenhouse crop planning, multi-agent rollout maximized total farmer income and fairness but had the highest runtime; agent-by-agent optimization was a middle ground, and independent Q-learning lagged in coordination.
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Comparative Analysis of Multi-Agent Reinforcement Learning Policies for Crop Planning Decision Support
In simulations of greenhouse crop planning, multi-agent rollout maximized total farmer income and fairness but had the highest runtime; agent-by-agent optimization was a middle ground, and independent Q-learning lagged in coordination.