We study the problem of how to repeatedly sell to a buyer running a no-regret,mean-based algorithm. Previous work [Braverman et al., 2018] shows that it ispossible to design effective mechanisms in such a setting that extract almost allof the economic surplus, but these mechanisms require the buyer's values each

For eachstimulus,ourextractedcomputational mechanisms areconsistent withprior scientific literature, and in one case yields a new mechanistic hypothesis.

As a result, existing studies have clear limitations whenscaledtoaworldwidecontext.

We introduce a principled method for performing zero-shot transfer in reinforcement learning (RL) by exploiting approximate models of the environment. Zero-shot transfer in RL has been investigated by leveraging methods rooted in generalized policy improvement (GPI) and successor features (SFs).

We seek to address the limitations of existing datasets in this area. Many such datasets lack a coherent traffic network graph to describe the relationship between sensors.

We give a model of how to infer natural language rules by doing experiments.

Neural Information Processing Systems http://nips.cc/

We contend that utilizing existing relational databases is a promising approach to construct a benchmark that has both merits.

Neural Information Processing Systems http://nips.cc/