Entropy-regularized Point-based Value Iteration
Delecki, Harrison, Vazquez-Chanlatte, Marcell, Yel, Esen, Wray, Kyle, Arnon, Tomer, Witwicki, Stefan, Kochenderfer, Mykel J.
–arXiv.org Artificial Intelligence
Model-based planners for partially observable problems must accommodate both model uncertainty during planning and goal uncertainty during objective inference. However, model-based planners may be brittle under these types of uncertainty because they rely on an exact model and tend to commit to a single optimal behavior. Inspired by results in the model-free setting, we propose an entropy-regularized model-based planner for partially observable problems. Entropy regularization promotes policy robustness for planning and objective inference by encouraging policies to be no more committed to a single action than necessary. We evaluate the robustness and objective inference performance of entropy-regularized policies in three problem domains. Our results show that entropy-regularized policies outperform non-entropy-regularized baselines in terms of higher expected returns under modeling errors and higher accuracy during objective inference.
arXiv.org Artificial Intelligence
Feb-14-2024
- Country:
- North America > United States > California > Santa Clara County (0.29)
- Genre:
- Research Report > New Finding (0.54)