Writing a program that performs well in a complex environment is a challenging task. In such problems, a method of deterministic programming combined with reinforcement learning (RL) can be helpful. However, current systems either force developers to encode knowledge in very specific forms (e.g., state-action features), or assume advanced RL...
Automatic transfer of learned knowledge from one task or domain to another offers great potential to simplify and expedite
the construction and deployment of intelligent systems.
In practice however, there are many barriers to achieving this
goal. In this article, we present a prototype system for the
real-world context of...