Tuning the Hyperparameters of Anytime Planning: A Metareasoning Approach with Deep Reinforcement Learning ICAPS 2022

Bhatia, A., Svegliato, J., Nashed, S. B., & Zilberstein, S. (2022). In Proceedings of the International Conference on Automated Planning and Scheduling. URL PDF

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.

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