Tuning the Hyperparameters of Anytime Planning: A Metareasoning Approach with Deep Reinforcement Learning
Published in Proceedings of the International Conference on Automated Planning and Scheduling, 2022
TL;DR: Deep RL to determine optimal stopping point and hyperparameters of anytime algorithms at runtime to optimize utility of the final solution. Good results on Anytime A* search algorithm and RRT* motion planning algorithm. Read more
Recommended citation: Bhatia, A., Svegliato, J., Nashed, S. B., & Zilberstein, S. (2022). Tuning the Hyperparameters of Anytime Planning: A Metareasoning Approach with Deep Reinforcement Learning. In Proceedings of the International Conference on Automated Planning and Scheduling, 32(1), 556-564. https://ojs.aaai.org/index.php/ICAPS/article/view/19842