Jason R Brown

This is the second of two posts resulting from a recent Astra/MATS research project investigating exploration hacking in AI debate. They are designed to be standalone, but we encourage interested readers to read both. The first focuses on our empirical results, this post focuses on a new conceptual framework. Authors Jason Brown*, Nathalie Kirch*, Joschka Braun, Helen Yannakoudakis, Roland S. Zimmermann, David Lindner *Equal contribution. TL;DR Exploration hacking is typically defined as a training-aware agent strategically altering its exploration during RL training to influence its own training outcome. We take a broader view of exploration hacking, treating it as an example of an undesired behaviour and analysing the direct mechanism of RL that removes such behaviours. This mechanism has five stages: (1) training must sample inputs that could elicit the behaviour, (2) the agent must sometimes deviate from it, (3) those failures must change the reward, (4) the reward change must cause a policy update, and (5) the update must generalise beyond the inputs it was made on. If any one stage fails, the behaviour can survive—and stages can fail through ordinary flaws in the RL setup, without any strategic effort by the agent. We explore properties of the RL setup relevant at each stage, and where possible provide links to empirical evidence or prior discussion. Using our ontology, we recommend an intuitive approach for identifying and removing persistent undesired behaviours in RL, inspired by the process we used to understand our own empirical results. In an appendix we also provide an overview of potential mitigations for exploration hacking, and how we see them interacting with the properties identified in our framework. Introduction Exploration hacking (EH) is typically defined as a training-aware agent strategically altering its exploration during RL training in order to influence the subsequent training outcome. For example, an AI might intentionally select