Causally Correct Partial Models for Reinforcement Learning

ICLR 2020 Danilo J. RezendeIvo DanihelkaGeorge PapamakariosNan Rosemary KeRay JiangTheophane WeberKarol GregorHamza MerzicFabio ViolaJane WangJovana MitrovicFrederic BesseIoannis AntonoglouLars Buesing

In reinforcement learning, we can learn a model of future observations and rewards, and use it to plan the agent's next actions. However, jointly modeling future observations can be computationally expensive or even intractable if the observations are high-dimensional (e.g. images)... (read more)

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