  A broad challenge of research on generalization for sequential
decision-making tasks in interactive environments is designing benchmarks that
clearly landmark progress. While there has been notable headway, current
benchmarks either do not provide suitable exposure nor intuitive control of the
underlying factors, are not easy-to-implement, customizable, or extensible, or
are computationally expensive to run. We built the Sandbox Environment for
Generalizable Agent Research (SEGAR) with all of these things in mind. SEGAR
improves the ease and accountability of generalization research in RL, as
generalization objectives can be easy designed by specifying task
distributions, which in turns allows the researcher to measure the nature of
the generalization objective. We present an overview of SEGAR and how it
contributes to these goals, as well as experiments that demonstrate a few types
of research questions SEGAR can help answer.
