Case-based Reasoning for Better Generalization in Textual Reinforcement Learning
This addresses generalization issues in textual reinforcement learning for researchers and practitioners, though it is incremental as it builds on existing neural agents.
The paper tackles the problem of poor generalization in text-based games under distributional shifts by proposing a case-based reasoning method that reuses past positive experiences. The approach consistently improves existing methods, achieves good out-of-distribution generalization, and sets new state-of-the-art results on widely used environments.
Text-based games (TBG) have emerged as promising environments for driving research in grounded language understanding and studying problems like generalization and sample efficiency. Several deep reinforcement learning (RL) methods with varying architectures and learning schemes have been proposed for TBGs. However, these methods fail to generalize efficiently, especially under distributional shifts. In a departure from deep RL approaches, in this paper, we propose a general method inspired by case-based reasoning to train agents and generalize out of the training distribution. The case-based reasoner collects instances of positive experiences from the agent's interaction with the world in the past and later reuses the collected experiences to act efficiently. The method can be applied in conjunction with any existing on-policy neural agent in the literature for TBGs. Our experiments show that the proposed approach consistently improves existing methods, obtains good out-of-distribution generalization, and achieves new state-of-the-art results on widely used environments.