Playing Atari Ball Games with Hierarchical Reinforcement Learning
  Human beings are particularly good at reasoning and inference from just a few
examples. When facing new tasks, humans will leverage knowledge and skills
learned before, and quickly integrate them with the new task. In addition to
learning by experimentation, human also learn socio-culturally through
instructions and learning by example. In this way humans can learn much faster
compared with most current artificial intelligence algorithms in many tasks. In
this paper, we test the idea of speeding up machine learning through social
learning. We argue that in solving real-world problems, especially when the
task is designed by humans, and/or for humans, there are typically instructions
from user manuals and/or human experts which give guidelines on how to better
accomplish the tasks. We argue that these instructions have tremendous value in
designing a reinforcement learning system which can learn in human fashion, and
we test the idea by playing the Atari games Tennis and Pong. We experimentally
demonstrate that the instructions provide key information about the task, which
can be used to decompose the learning task into sub-systems and construct
options for the temporally extended planning, and dramatically accelerate the
learning process.
