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An open benchmark in GreenLight-Gym2 compares four greenhouse controllers, revealing why model-free RL (PPO) underperforms hand-tuned rules and how MPC and hybrid RL-in-MPC approaches can help.

Using an FPV drone RL project as a case study, this guide covers reward shaping principles, Bang-Bang control hacking, module isolation, single-variable debugging, and behavior visualization to solve common RL training issues.