  We propose RAPid-Learn: Learning to Recover and Plan Again, a hybrid planning
and learning method, to tackle the problem of adapting to sudden and unexpected
changes in an agent's environment (i.e., novelties). RAPid-Learn is designed to
formulate and solve modifications to a task's Markov Decision Process (MDPs)
on-the-fly and is capable of exploiting domain knowledge to learn any new
dynamics caused by the environmental changes. It is capable of exploiting the
domain knowledge to learn action executors which can be further used to resolve
execution impasses, leading to a successful plan execution. This novelty
information is reflected in its updated domain model. We demonstrate its
efficacy by introducing a wide variety of novelties in a gridworld environment
inspired by Minecraft, and compare our algorithm with transfer learning
baselines from the literature. Our method is (1) effective even in the presence
of multiple novelties, (2) more sample efficient than transfer learning RL
baselines, and (3) robust to incomplete model information, as opposed to pure
symbolic planning approaches.

Artificial Intelligence;Machine Learning