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Offline RL algorithms must account for the fact that the dataset they areprovided may leave many facets of the environment unknown. The most common wayto approach this challenge is to employ pessimistic or conservative methods,which avoid behaviors that are too dissimilar from those in the trainingdataset. However, relying exclusively on conservatism has drawbacks:performance is sensitive to the exact degree of conservatism, and conservativeobjectives can recover highly suboptimal policies. In this work, we proposethat offline RL methods should instead be adaptive in the presence ofuncertainty. We show that acting optimally in offline RL in a Bayesian senseinvolves solving an implicit POMDP. As a result, optimal policies for offlineRL must be adaptive, depending not just on the current state but rather all thetransitions seen so far during evaluation.We present a model-free algorithm forapproximating this optimal adaptive policy, and demonstrate the efficacy oflearning such adaptive policies in offline RL benchmarks.

Offline RL Policies Should be Trained to be Adaptive