Conceptually, our approach exploits the structural information of composite tasks for efficient exploration.
in order to mitigate the reward sparsity issue, we equip our agent with an evaluation module (internal critic) that gives intrinsic reward signals, indicating how likely a particular subtask is completed based on its current state generated by the global state tracker.