# Machine Teaching in Hierarchical Genetic Reinforcement Learning: Curriculum Design of Reward Functions for Swarm Shepherding

arXiv: https://arxiv.org/abs/1901.00949 · HTML: https://mlsanity.com/paper/1901.00949/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jan 2019.

## Built on by (most cited first, 3 of 3)

- [Multi-Phase Multi-Objective Dexterous Manipulation with Adaptive Hierarchical Curriculum](https://mlsanity.com/paper/2205.13441/) (2022)
- [Physics-Guided Hierarchical Reward Mechanism for Learning-Based Robotic Grasping](https://mlsanity.com/paper/2205.13561/) (2022)
- [Developing Decentralised Resilience to Malicious Influence in Collective Perception Problem](https://mlsanity.com/paper/2211.03063/) (2022)

## Abstract (arXiv)

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.