  Sparsity of rewards while applying a deep reinforcement learning method
negatively affects its sample-efficiency. A viable solution to deal with the
sparsity of rewards is to learn via intrinsic motivation which advocates for
adding an intrinsic reward to the reward function to encourage the agent to
explore the environment and expand the sample space. Though intrinsic
motivation methods are widely used to improve data-efficient learning in the
reinforcement learning model, they also suffer from the so-called detachment
problem. In this article, we discuss the limitations of intrinsic curiosity
module in sparse-reward multi-agent reinforcement learning and propose a method
called I-Go-Explore that combines the intrinsic curiosity module with the
Go-Explore framework to alleviate the detachment problem.
