Extending Policy Shaping to Continuous State Spaces (Student Abstract)
Keywords:Reinforcement Learning, Human Robot Interaction, Machine Learning
AbstractPolicy Shaping is a Human-in-the-loop Reinforcement Learning (HRL) algorithm. We extend this work to continuous states with our algorithm, Deep Policy Shaping (DPS). DPS uses a feedback neural network that learns the optimality of actions from noisy feedback combined with an RL algorithm. In simulation, we find that DPS outperforms or matches baselines averaged over multiple hyperparameter settings and varying feedback correctness.
How to Cite
Wei, T., Faulkner, T. A. K., & Thomaz, A. L. (2021). Extending Policy Shaping to Continuous State Spaces (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 15919-15920. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17956
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