Encoding Human Domain Knowledge to Warm Start Reinforcement Learning

Authors

  • Andrew Silva Georgia Institute of Technology
  • Matthew Gombolay Georgia Institute of Technology

Keywords:

Neuro-Symbolic AI (NSAI), Reinforcement Learning, Human-in-the-loop Machine Learning

Abstract

Deep reinforcement learning has been successful in a variety of tasks, such as game playing and robotic manipulation. However, attempting to learn tabula rasa disregards the logical structure of many domains as well as the wealth of readily available knowledge from domain experts that could help "warm start" the learning process. We present a novel reinforcement learning technique that allows for intelligent initialization of a neural network weights and architecture. Our approach permits the encoding domain knowledge directly into a neural decision tree, and improves upon that knowledge with policy gradient updates. We empirically validate our approach on two OpenAI Gym tasks and two modified StarCraft 2 tasks, showing that our novel architecture outperforms multilayer-perceptron and recurrent architectures. Our knowledge-based framework finds superior policies compared to imitation learning-based and prior knowledge-based approaches. Importantly, we demonstrate that our approach can be used by untrained humans to initially provide >80% increase in expected reward relative to baselines prior to training (p < 0.001), which results in a >60% increase in expected reward after policy optimization (p = 0.011).

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Published

2021-05-18

How to Cite

Silva, A., & Gombolay, M. (2021). Encoding Human Domain Knowledge to Warm Start Reinforcement Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 35(6), 5042-5050. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16638

Issue

Section

AAAI Technical Track Focus Area on Neuro-Symbolic AI