Deep Hierarchical Reinforcement Learning Algorithm in Partially
  Observable Markov Decision Processes
  In recent years, reinforcement learning has achieved many remarkable
successes due to the growing adoption of deep learning techniques and the rapid
growth in computing power. Nevertheless, it is well-known that flat
reinforcement learning algorithms are often not able to learn well and
data-efficient in tasks having hierarchical structures, e.g. consisting of
multiple subtasks. Hierarchical reinforcement learning is a principled approach
that is able to tackle these challenging tasks. On the other hand, many
real-world tasks usually have only partial observability in which state
measurements are often imperfect and partially observable. The problems of RL
in such settings can be formulated as a partially observable Markov decision
process (POMDP). In this paper, we study hierarchical RL in POMDP in which the
tasks have only partial observability and possess hierarchical properties. We
propose a hierarchical deep reinforcement learning approach for learning in
hierarchical POMDP. The deep hierarchical RL algorithm is proposed to apply to
both MDP and POMDP learning. We evaluate the proposed algorithm on various
challenging hierarchical POMDP.
