RAPid-Learn: A Framework for Learning to Recover for Handling Novelties in Open-World Environments.

Abstract

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. 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. 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.