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Reinforcement learning (RL) has shown great promise with algorithms learningin environments with large state and action spaces purely from scalar rewardsignals. A crucial challenge for current deep RL algorithms is that theyrequire a tremendous amount of environment interactions for learning. This canbe infeasible in situations where such interactions are expensive; such as inrobotics. Offline RL algorithms try to address this issue by bootstrapping thelearning process from existing logged data without needing to interact with theenvironment from the very beginning. While online RL algorithms are typicallyevaluated as a function of the number of environment interactions, there existsno single established protocol for evaluating offline RL methods.In this paper,we propose a sequential approach to evaluate offline RL algorithms as afunction of the training set size and thus by their data efficiency. Sequentialevaluation provides valuable insights into the data efficiency of the learningprocess and the robustness of algorithms to distribution changes in the datasetwhile also harmonizing the visualization of the offline and online learningphases. Our approach is generally applicable and easy to implement. We compareseveral existing offline RL algorithms using this approach and present insightsfrom a variety of tasks and offline datasets.

Bridging the Gap Between Offline and Online Reinforcement Learning Evaluation Methodologies