Posted as part of the AI Alignment Forum sequence on Value Learning.

Rohin's note: While I motivated the last post with an example of using a specific model for human biases, in this post (original here), Jacob Steinhardt and Owain Evans point out that model mis-specification can arise in other parts of inverse reinforcement learning as well. The arguments here consider some more practical concerns (for example, the worries about getting only short-term data for each human would not be a problem if you had the entire human policy).

In my previous post, “Latent Variables and Model Mis-specification”, I argued that while machine learning is good at optimizing accuracy on observed signals, it has less to say about correctly inferring the values for unobserved variables in a model. In this post I’d like to focus in on a specific context for this: inverse reinforcement learning (Ng et al. 2000, Abbeel et al. 2004, Ziebart et al. 2008, Ho et al 2016), where one observes the actions of an agent and wants to infer the preferences and beliefs that led to those actions. For this post, I am pleased to be joined by Owain Evans, who is an active researcher in this area and has co-authored an online book about building models of agents (see here in particular for a tutorial on inverse reinforcement learning and inverse planning).

Owain and I are particularly interested in inverse reinforcement learning (IRL) because it has been proposed (most notably by Stuart Russell) as a method for learning human values in the context of AI safety; among other things, this would eventually involve learning and correctly implementing human values by artificial agents that are much more powerful, and act with much broader scope, than any humans alive today. While we think that overall IRL is a promising route to consider, we believe that there are also a number of non-obvious pitfalls related to performing IRL with a mis-specified model. The role of IRL in AI safety is to infer human values, which are represented by a reward function or utility function. But crucially, human values (or human reward functions) are never directly observed.

Below, we elaborate on these issues. We hope that by being more aware of these issues, researchers working on inverse reinforcement learning can anticipate and address the resulting failure modes. In addition, we think that considering issues caused by model...

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