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Reinforcement Learning and Control as Probabilistic Inference

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Reinforcement learning and inference offer powerful frameworks for solving sequential decision-making problems. While classically reinforcement learning and inference have been studied independently, it is possible to frame the decision-making problem itself as inference in a graphical model. This formalism allows us to utilize well-known approximate inference techniques and gives insights into how we can extend the model. The basic underlying framework has been proposed in the literature in several forms before, and remains an important source of inspiration for novel algorithms.

This talk is part of the Machine Learning Reading Group @ CUED series.

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