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In Search of Robust Measures of Generalization
This work addresses the question of how to evaluate generalization bounds empirically and argues that generalization measures should instead be evaluated within the framework of distributional robustness.
Pretraining Representations for Data-Efficient Reinforcement Learning
This work uses unlabeled data to pretrain an encoder which is then finetuned on a small amount of task-specific data, and employs a combination of latent dynamics modelling and unsupervised goal-conditioned RL to encourage learning representations which capture diverse aspects of the underlying MDP.