Orgs

Model-based value expansion (MVE) was first proposed by [cite/t:@feinbergModelBasedValueEstimation2018]. They expand the critic's target value using a deterministic dynamics model. Stochastic ensemble value expansion (STEVE) [cite:@buckmanSampleEfficientReinforcementLearning2018] extend MVE by learning a dynamics model with an ensemble of probabilistic NNs.

Vision language models (VLMs) that have been trained on internet-scale text-image data sets have been used to formulate reward functions from language [cite:@kwonRewardDesignLanguage2022;@rocamondeVisionLanguageModelsAre2023;@mahmoudiehZeroShotRewardSpecification2022]. For example, [cite/t:@mahmoudiehZeroShotRewardSpecification2022];[cite/t:@rocamondeVisionLanguageModelsAre2023] use CLIP embeddings [cite:@radfordLearningTransferableVisual2021] to calculate the similarity between an image observation embedding and the goal text embedding.