  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 interferes with learning. By adding a
regularized loss that penalizes correlation in latent features (with only
slight computation), we decorrelate features represented by deep neural
networks incrementally. On 49 Atari games, with the same regularization factor,
our decorrelation algorithms perform $70\%$ in terms of human-normalized
scores, which is $40\%$ better than DQN. In particular, ours performs better
than DQN on 39 games with 4 close ties and lost only slightly on $6$ games.
Empirical results also show that the decorrelation method applies to Quantile
Regression DQN (QR-DQN) and significantly boosts performance. Further
experiments on the losing games show that our decorelation algorithms can win
over DQN and QR-DQN with a fined tuned regularization factor.
