• Corpus ID: 235732066

Low-Dimensional State and Action Representation Learning with MDP Homomorphism Metrics

  title={Low-Dimensional State and Action Representation Learning with MDP Homomorphism Metrics},
  author={Nicol{\`o} Botteghi and Mannes Poel and Beril Sirmaçek and Christoph Brune},
Deep Reinforcement Learning has shown its ability in solving complicated problems directly from high-dimensional observations. However, in end-to-end settings, Reinforcement Learning algorithms are not sample-efficient and requires long training times and quantities of data. In this work, we proposed a framework for sample-efficient Reinforcement Learning that take advantage of state and action representations to transform a high-dimensional problem into a low-dimensional one. Moreover, we seek… 

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