  Current imitation learning techniques are too restrictive because they
require the agent and expert to share the same action space. However,
oftentimes agents that act differently from the expert can solve the task just
as good. For example, a person lifting a box can be imitated by a ceiling
mounted robot or a desktop-based robotic-arm. In both cases, the end goal of
lifting the box is achieved, perhaps using different strategies. We denote this
setup as \textit{Inspiration Learning} - knowledge transfer between agents that
operate in different action spaces. Since state-action expert demonstrations
can no longer be used, Inspiration learning requires novel methods to guide the
agent towards the end goal. In this work, we rely on ideas of Preferential
based Reinforcement Learning (PbRL) to design Advantage Actor-Critic algorithms
for solving inspiration learning tasks. Unlike classic actor-critic
architectures, the critic we use consists of two parts: a) a state-value
estimation as in common actor-critic algorithms and b) a single step reward
function derived from an expert/agent classifier. We show that our method is
capable of extending the current imitation framework to new horizons. This
includes continuous-to-discrete action imitation, as well as primitive-to-macro
action imitation.
