Can Increasing Input Dimensionality Improve Deep Reinforcement Learning?

ICML 2020 Kei OtaTomoaki OikiDevesh K. JhaToshisada MariyamaDaniel Nikovski

Deep reinforcement learning (RL) algorithms have recently achieved remarkable successes in various sequential decision making tasks, leveraging advances in methods for training large deep networks. However, these methods usually require large amounts of training data, which is often a big problem for real-world applications... (read more)

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