Learning Reusable Options for Multi-Task Reinforcement Learning
  Reinforcement learning (RL) has become an increasingly active area of
research in recent years. Although there are many algorithms that allow an
agent to solve tasks efficiently, they often ignore the possibility that prior
experience related to the task at hand might be available. For many practical
applications, it might be unfeasible for an agent to learn how to solve a task
from scratch, given that it is generally a computationally expensive process;
however, prior experience could be leveraged to make these problems tractable
in practice. In this paper, we propose a framework for exploiting existing
experience by learning reusable options. We show that after an agent learns
policies for solving a small number of problems, we are able to use the
trajectories generated from those policies to learn reusable options that allow
an agent to quickly learn how to solve novel and related problems.
