  Novelty adaptation is the ability of an intelligent agent to adjust its
behavior in response to changes in its environment. This is an important
characteristic of intelligent agents, as it allows them to continue to function
effectively in novel or unexpected situations, but still stands as a critical
challenge for deep reinforcement learning (DRL). To tackle this challenge, we
propose a simple yet effective novel method, NAPPING (Novelty Adaptation
Principles Learning), that allows trained DRL agents to respond to different
classes of novelties in open worlds rapidly. With NAPPING, DRL agents can learn
to adjust the trained policy only when necessary. They can quickly generalize
to similar novel situations without affecting the part of the trained policy
that still works. To demonstrate the efficiency and efficacy of NAPPING, we
evaluate our method on four action domains that are different in reward
structures and the type of task. The domains are CartPole and MountainCar
(classic control), CrossRoad (path-finding), and AngryBirds (physical
reasoning). We compare NAPPING with standard online and fine-tuning DRL methods
in CartPole, MountainCar and CrossRoad, and state-of-the-art methods in the
more complicated AngryBirds domain. Our evaluation results demonstrate that
with our proposed method, DRL agents can rapidly and effectively adjust to a
wide range of novel situations across all tested domains.
