  In recent years, the robotics community has made substantial progress in
robotic manipulation using deep reinforcement learning (RL). Effectively
learning of long-horizon tasks remains a challenging topic. Typical RL-based
methods approximate long-horizon tasks as Markov decision processes and only
consider current observation (images or other sensor information) as input
state. However, such approximation ignores the fact that skill-sequence also
plays a crucial role in long-horizon tasks. In this paper, we take both the
observation and skill sequences into account and propose a
skill-sequence-dependent hierarchical policy for solving a typical long-horizon
task. The proposed policy consists of a high-level skill policy (utilizing
skill sequences) and a low-level parameter policy (responding to observation)
with corresponding training methods, which makes the learning much more
sample-efficient. Experiments in simulation demonstrate that our approach
successfully solves a long-horizon task and is significantly faster than
Proximal Policy Optimization (PPO) and the task schema methods.
