Talk Sessions:

Poster Sessions:

June 22, Booth 40

June 23, Booth 41

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

Abhinav Bhatia, Justin Svegliato, Samer Nashed and Shlomo Zilberstein

Abstract: Anytime planning algorithms often have hyperparameters that can be tuned at runtime to optimize their performance. While work on metareasoning has focused on when to interrupt an anytime planner and act on the current plan, the scope of metareasoning can be expanded to tuning the hyperparameters of the anytime planner at runtime. This paper introduces a general, decision-theoretic metareasoning approach that optimizes both the stopping point and hyperparameters of anytime planning. We begin by proposing a generalization of the standard meta-level control problem for anytime algorithms. We then offer a meta-level control technique that monitors and controls an anytime algorithm using deep reinforcement learning. Finally, we show that our approach boosts performance on a common benchmark domain that uses anytime weighted A* to solve a range of heuristic search problems and a mobile robot application that uses RRT* to solve motion planning problems.

*This password protected talk video will only be available after it was presented at the conference.