Biologically inspired architectures for sample-efficient deep reinforcement learning

25 Nov 2019Pierre H. RichemondArinbjörn KolbeinssonYike Guo

Deep reinforcement learning requires a heavy price in terms of sample efficiency and overparameterization in the neural networks used for function approximation. In this work, we use tensor factorization in order to learn more compact representation for reinforcement learning policies... (read more)

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