  The aim of this paper is to demonstrate the efficacy of using Contrastive
Random Walk as a curiosity method to achieve faster convergence to the optimal
policy.Contrastive Random Walk defines the transition matrix of a random walk
with the help of neural networks. It learns a meaningful state representation
with a closed loop. The loss of Contrastive Random Walk serves as an intrinsic
reward and is added to the environment reward. Our method works well in
non-tabular sparse reward scenarios, in the sense that our method receives the
highest reward within the same iterations compared to other methods. Meanwhile,
Contrastive Random Walk is more robust. The performance doesn't change much
with different random initialization of environments. We also find that
adaptive restart and appropriate temperature are crucial to the performance of
Contrastive Random Walk.
