Hierarchical abstraction and curiosity-driven exploration are two common paradigms in current reinforcement learning approaches to break down difficult problems into a sequence of simpler ones and to overcome reward sparsity.
Existing leading code comment generation approaches with the structure-to-sequence framework ignores the type information of the interpretation of the code, e. g., operator, string, etc.
Recent adaptations of the toolkit either utilize Abstract Task Representations (ATRs) to solve Non-Observable (NO) tasks or storage of past input features to solve Partially-Observable (PO) tasks, but not both.
Hierarchical Reinforcement Learning (HRL) has held the promise to enhance the capabilities of RL agents via operation on different levels of temporal abstraction.
Hierarchical reinforcement learning (HRL) helps address large-scale and sparse reward issues in reinforcement learning.
For each subtask, the manager is trained to set a subgoal only by a task-level binary label, which requires much fewer labels than conventional approaches.
The results support hierarchical RL as a plausible model of task interleaving.
Many approaches to hierarchical reinforcement learning aim to identify sub-goal structure in tasks.
Hierarchical reinforcement learning is a promising approach to tackle long-horizon decision-making problems with sparse rewards.