Model-Based Offline Reinforcement Learning With Adversarial Data Augmentation

Model-based offline reinforcement learning (RL, where an AI learns to make decisions from a fixed dataset without interacting with a live environment) struggles because static data makes it hard to develop robust policies. This paper introduces MORAL, which uses adversarial data augmentation (a technique where competing AI models deliberately generate challenging training examples to improve robustness) to dynamically enrich training data and improve policy learning instead of using traditional fixed rollout methods.