Coarticulation in Markov Decision Processes
Abstract
We investigate an approach for simultaneously committing to mul- tiple activities, each modeled as a temporally extended action in a semi-Markov decision process (SMDP). For each activity we de- fine a set of admissible solutions consisting of the redundant set of optimal policies, and those policies that ascend the optimal state- value function associated with them. A plan is then generated by merging them in such a way that the solutions to the subordinate activities are realized in the set of admissible solutions satisfying the superior activities. We present our theoretical results and em- pirically evaluate our approach in a simulated domain.
Cite
Text
Rohanimanesh et al. "Coarticulation in Markov Decision Processes." Neural Information Processing Systems, 2004.Markdown
[Rohanimanesh et al. "Coarticulation in Markov Decision Processes." Neural Information Processing Systems, 2004.](https://mlanthology.org/neurips/2004/rohanimanesh2004neurips-coarticulation/)BibTeX
@inproceedings{rohanimanesh2004neurips-coarticulation,
title = {{Coarticulation in Markov Decision Processes}},
author = {Rohanimanesh, Khashayar and Platt, Robert and Mahadevan, Sridhar and Grupen, Roderic},
booktitle = {Neural Information Processing Systems},
year = {2004},
pages = {1137-1144},
url = {https://mlanthology.org/neurips/2004/rohanimanesh2004neurips-coarticulation/}
}