  It remains a significant challenge to train generally capable agents with
reinforcement learning (RL). A promising avenue for improving the robustness of
RL agents is through the use of curricula. One such class of methods frames
environment design as a game between a student and a teacher, using
regret-based objectives to produce environment instantiations (or levels) at
the frontier of the student agent's capabilities. These methods benefit from
their generality, with theoretical guarantees at equilibrium, yet they often
struggle to find effective levels in challenging design spaces. By contrast,
evolutionary approaches seek to incrementally alter environment complexity,
resulting in potentially open-ended learning, but often rely on domain-specific
heuristics and vast amounts of computational resources. In this paper we
propose to harness the power of evolution in a principled, regret-based
curriculum. Our approach, which we call Adversarially Compounding Complexity by
Editing Levels (ACCEL), seeks to constantly produce levels at the frontier of
an agent's capabilities, resulting in curricula that start simple but become
increasingly complex. ACCEL maintains the theoretical benefits of prior
regret-based methods, while providing significant empirical gains in a diverse
set of environments. An interactive version of the paper is available at
accelagent.github.io.

Machine Learning