Stop! planner time: metareasoning for probabilistic planning using learned performance profiles
The metareasoning framework aims to enable autonomous agents to factor in planning costs when making decisions. In this work, we develop the first non-myopic metareasoning algorithm for planning with Markov decision processes. Our method learns the behaviour of anytime probabilistic planning algorit...
| Main Authors: | , , |
|---|---|
| Format: | Conference item |
| Language: | English |
| Published: |
Association for the Advancement of Artificial Intelligence
2024
|