  Navigating deceptive domains has often been a challenge in machine learning
due to search algorithms getting stuck at sub-optimal local optima. Many
algorithms have been proposed to navigate these domains by explicitly
maintaining diversity or equivalently promoting exploration, such as Novelty
Search or other so-called Quality Diversity algorithms. In this paper, we
present an approach with promise to solve deceptive domains without explicit
diversity maintenance by optimizing a potentially large set of defined
objectives. These objectives can be extracted directly from the environment by
sub-aggregating the raw performance of individuals in a variety of ways. We use
lexicase selection to optimize for these objectives as it has been shown to
implicitly maintain population diversity. We compare this technique with a
varying number of objectives to a commonly used quality diversity algorithm,
MAP-Elites, on a set of discrete optimization as well as reinforcement learning
domains with varying degrees of deception. We find that decomposing objectives
into many objectives and optimizing them outperforms MAP-Elites on the
deceptive domains that we explore. Furthermore, we find that this technique
results in competitive performance on the diversity-focused metrics of QD-Score
and Coverage, without explicitly optimizing for these things. Our ablation
study shows that this technique is robust to different subaggregation
techniques. However, when it comes to non-deceptive, or ``illumination"
domains, quality diversity techniques generally outperform our objective-based
framework with respect to exploration (but not exploitation), hinting at
potential directions for future work.
