Machine Teaching in Hierarchical Genetic Reinforcement Learning:
  Curriculum Design of Reward Functions for Swarm Shepherding
  The design of reward functions in reinforcement learning is a human skill
that comes with experience. Unfortunately, there is not any methodology in the
literature that could guide a human to design the reward function or to allow a
human to transfer the skills developed in designing reward functions to another
human and in a systematic manner. In this paper, we use Systematic
Instructional Design, an approach in human education, to engineer a machine
education methodology to design reward functions for reinforcement learning. We
demonstrate the methodology in designing a hierarchical genetic reinforcement
learner that adopts a neural network representation to evolve a swarm
controller for an agent shepherding a boids-based swarm. The results reveal
that the methodology is able to guide the design of hierarchical reinforcement
learners, with each model in the hierarchy learning incrementally through a
multi-part reward function. The hierarchy acts as a decision fusion function
that combines the individual behaviours and skills learnt by each instruction
to create a smart shepherd to control the swarm.
