  Sparse representations have been shown to be useful in deep reinforcement
learning for mitigating catastrophic interference and improving the performance
of agents in terms of cumulative reward. Previous results were based on a two
step process were the representation was learned offline and the action-value
function was learned online afterwards. In this paper, we investigate if it is
possible to learn a sparse representation and the action-value function
simultaneously and incrementally. We investigate this question by employing
several regularization techniques and observing how they affect sparsity of the
representation learned by a DQN agent in two different benchmark domains. Our
results show that with appropriate regularization it is possible to increase
the sparsity of the representations learned by DQN agents. Moreover, we found
that learning sparse representations also resulted in improved performance in
terms of cumulative reward. Finally, we found that the performance of the
agents that learned a sparse representation was more robust to the size of the
experience replay buffer. This last finding supports the long standing
hypothesis that the overlap in representations learned by deep neural networks
is the leading cause of catastrophic interference.
