Iterative Value-Aware Model Learning

NeurIPS 2018 Amir-Massoud Farahmand

This paper introduces a model-based reinforcement learning (MBRL) framework that incorporates the underlying decision problem in learning the transition model of the environment. This is in contrast with conventional approaches to MBRL that learn the model of the environment, for example by finding the maximum likelihood estimate, without taking into account the decision problem... (read more)

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