Temporal-adaptive Hierarchical Reinforcement Learning
  Hierarchical reinforcement learning (HRL) helps address large-scale and
sparse reward issues in reinforcement learning. In HRL, the policy model has an
inner representation structured in levels. With this structure, the
reinforcement learning task is expected to be decomposed into corresponding
levels with sub-tasks, and thus the learning can be more efficient. In HRL,
although it is intuitive that a high-level policy only needs to make macro
decisions in a low frequency, the exact frequency is hard to be simply
determined. Previous HRL approaches often employed a fixed-time skip strategy
or learn a terminal condition without taking account of the context, which,
however, not only requires manual adjustments but also sacrifices some decision
granularity. In this paper, we propose the \emph{temporal-adaptive hierarchical
policy learning} (TEMPLE) structure, which uses a temporal gate to adaptively
control the high-level policy decision frequency. We train the TEMPLE structure
with PPO and test its performance in a range of environments including 2-D
rooms, Mujoco tasks, and Atari games. The results show that the TEMPLE
structure can lead to improved performance in these environments with a
sequential adaptive high-level control.
