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Deep Reinforcement Learning with Decorrelation
[article]
2019
arXiv
pre-print
Learning an effective representation for high-dimensional data is a challenging problem in reinforcement learning (RL). Deep reinforcement learning (DRL) such as Deep Q networks (DQN) achieves remarkable success in computer games by learning deeply encoded representation from convolution networks. In this paper, we propose a simple yet very effective method for representation learning with DRL algorithms. Our key insight is that features learned by DRL algorithms are highly correlated, which
arXiv:1903.07765v3
fatcat:ohfgejpvqzdsbauzqj4yr56n3m