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

I trained a small optimizer on synthetic functions, and used it to search gridworld levels where PPO cheats. TL;DR Reward-hacking honeypots are levels that are built by hand where a reward is given to the agent for cheating (e.g. taking a shortcut to the goal). I converted this process into a black box optimization problem where the score is based on how often PPO cheats. I used evolution strategies to train the optimizer on the cheap, synthetic functions, and applied it to the honeypot without any further training. Given the same PPO budget, my best policy (RLBO) found levels where PPO cheate

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• #fewer