Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is:Safe exploration and corrigibility , published by Evan Hubinger on the AI Alignment Forum. EDIT: I now think this post is somewhat confusing and would recommend starting with my more recent post “Exploring safe exploration.” Balancing exploration and exploitation is a classic problem in reinforcement learning. Historically—with approaches such as deep Q learning, for example—exploration is done explicitly via a rule such as ϵ -greedy exploration or Boltzmann exploration. With more modern approaches, however—especially policy gradient approaches like PPO that aren't amenable to something like Boltzmann exploration—the exploration is instead entirely learned, encouraged by some sort of extra term in the loss to implicitly encourage exploratory behavior. This is usually an entropy term, though other more advanced approaches have also been proposed, such as random network distillation in which the agent learns to explore states for which it would have a hard time predicting the output of a random neural network, an approach which was able to set a state of the art on Montezuma's Revenge, a notoriously difficult Atari environment because of how much exploration it requires. This move to learned exploration has a very interesting and important consequence, however, which is that the safe exploration problem for learned exploration becomes very different. Making ϵ -greedy exploration safe is in some sense quite easy, since the way it explores is totally random. If you assume that the policy without exploration is safe, then for ϵ -greedy exploration to be safe on average, it just needs to be the case that the environment is safe on average, which is just a standard engineering question. With learned exploration, however, this becomes much more complicated—there's no longer a nice “if the non-exploratory policy is safe” assumption that can be used to cleanly subdivide the overall problem of off-distribution safety, since it's just a single, learned policy doing both exploration and exploitation. First, though, an aside: why is learned exploration so much better? I think the answer lies primarily in the following observation: for most problems, exploration is an instrumental goal, not a terminal one, which means that to do exploration “right” you have to do it in a way which is cognizant of the objective you're trying to optimize for. Boltzmann exploration is better than ϵ -greedy exploration because its exploration is guided by its exploitation—but it's still essentially just adding random jitter to your policy. Fundamentally, though, exploration is about the value of information such that proper exploration requires dynamically balancing the value of information with the value of exploitation. Ideally, in this view, exploration should arise naturally as an instrumental goal of pursuing the given reward function—an agent should instrumentally want to get updated in such a way that causes it to become better at pursuing its current objective. Except, there's a really serious, major problem with that reasoning: instrumental exploration only cares about the value of information for helping the model to achieve the goal it's learned so far, not for helping it fix its goal to be more aligned with the actual goal.[1] Consider, for instance, my maze example. Instrumental exploration will help the model better explore the larger maze, but it won't help it better figure out that it's objective of finding the green arrow is misaligned—that is, it won't, for example, lead to the model trying both the green arrow and the end of the maze to see which one is right. Furthermore, because the instrumental exploration actively helps the model explore the larger maze better, it improves the model's capability generalization without also helping its objective generalization, leading to precisely...