Coarticulation in Markov Decision Processes

Part of Advances in Neural Information Processing Systems 17 (NIPS 2004)

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Khashayar Rohanimanesh, Robert Platt, Sridhar Mahadevan, Roderic Grupen


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.