Learning and Using Models

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Reinforcement Learning in Healthcare: A Survey (2019, methodology): “In the efficient techniques described in Section II-B, modelbased methods enable improved sample efficiency over modelfree methods by learning a model of the transition and reward functions of the domain on-line and then planing a policy using this model [43].”
Learning Multimodal Transition Dynamics for Model-Based Reinforcement Learning (2017, methodology): “Most work in model-based RL has either focussed on tabular learning [3], linear function approximation [1] with uni-modal Gaussian noise [20], or other deterministic function approximators like random forests [14].”
Learning non-myopically from human-generated reward (2013, methodology): “UCT-based search has been successful in tasks with especially large state spaces [8], originally in games like Go [2] but also in more general reinforcement learning tasks [3].”