  Bipedal robots are garnering increasing global attention due to their
potential applications and advancements in artificial intelligence,
particularly in Deep Reinforcement Learning (DRL). While DRL has driven
significant progress in bipedal locomotion, developing a comprehensive and
unified framework capable of adeptly performing a wide range of tasks remains a
challenge. This survey systematically categorizes, compares, and summarizes
existing DRL frameworks for bipedal locomotion, organizing them into end-to-end
and hierarchical control schemes. End-to-end frameworks are assessed based on
their learning approaches, whereas hierarchical frameworks are dissected into
layers that utilize either learning-based methods or traditional model-based
approaches. This survey provides a detailed analysis of the composition,
capabilities, strengths, and limitations of each framework type. Furthermore,
we identify critical research gaps and propose future directions aimed at
achieving a more integrated and efficient framework for bipedal locomotion,
with potential broad applications in everyday life.
