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We propose a new model-based offline RL framework, called Adversarial Modelsfor Offline Reinforcement Learning (ARMOR), which can robustly learn policiesto improve upon an arbitrary baseline policy regardless of data coverage. Basedon the concept of relative pessimism, ARMOR is designed to optimize for theworst-case relative performance when facing uncertainty. In theory, we provethat the learned policy of ARMOR never degrades the performance of the baselinepolicy with any admissible hyperparameter, and can learn to compete with thebest policy within data coverage when the hyperparameter is well tuned, and thebaseline policy is supported by the data. Such a robust policy improvementproperty makes ARMOR especially suitable for building real-world learningsystems, because in practice ensuring no performance degradation is imperativebefore considering any benefit learning can bring.

ARMOR: A Model-based Framework for Improving Arbitrary Baseline Policies with Offline Data