  Unlike reinforcement learning (RL) agents, humans remain capable multitaskers
in changing environments. In spite of only experiencing the world through their
own observations and interactions, people know how to balance focusing on tasks
with learning about how changes may affect their understanding of the world.
This is possible by choosing to solve tasks in ways that are interesting and
generally informative beyond just the current task. Motivated by this, we
propose an agent influence framework for RL agents to improve the adaptation
efficiency of external models in changing environments without any changes to
the agent's rewards. Our formulation is composed of two self-contained modules:
interest fields and behavior shaping via interest fields. We implement an
uncertainty-based interest field algorithm as well as a skill-sampling-based
behavior-shaping algorithm to use in testing this framework. Our results show
that our method outperforms the baselines in terms of external model adaptation
on metrics that measure both efficiency and performance.
