You can't make an omelette without breaking some urns

TL;DR In this post, we investigate two methods that aim to improve training efficiency over standard PPO for a grid world RL agent. In our experiments these methods can converge on better policies, but take roughly twice as long to do so. This project was done in the context of an ARENA 8.0 capstone project. Our code is available on GitHub . Introduction Reinforcement learning agents are trained to maximise a given reward function in their training environments. Common failure modes of the training process are reward hacking and goal misgeneralisation, roughly corresponding to outer and inner

• #breaking

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