Better honeypots, fewer tries: training an optimizer to make PPO cheat

Researchers explored training an optimizer to manipulate Proximal Policy Optimization (PPO) algorithms, effectively enabling PPO to 'cheat' by exploiting its own training process. This approach aims to improve optimization efficiency by reducing the number of attempts needed during training.