  Unsupervised pre-training has been on the lookout for the virtue of a value
function representation referred to as successor features (SFs), which
decouples the dynamics of the environment from the rewards. It has a
significant impact on the process of task-specific fine-tuning due to the
decomposition. However, existing approaches struggle with local optima due to
the unified intrinsic reward of exploration and exploitation without
considering the linear regression problem and the discriminator supporting a
small skill sapce. We propose a novel unsupervised pre-training model with SFs
based on a non-monolithic exploration methodology. Our approach pursues the
decomposition of exploitation and exploration of an agent built on SFs, which
requires separate agents for the respective purpose. The idea will leverage not
only the inherent characteristics of SFs such as a quick adaptation to new
tasks but also the exploratory and task-agnostic capabilities. Our suggested
model is termed Non-Monolithic unsupervised Pre-training with Successor
features (NMPS), which improves the performance of the original monolithic
exploration method of pre-training with SFs. NMPS outperforms Active
Pre-training with Successor Features (APS) in a comparative experiment.
