Title
Pond-Hindsight: Applying Hindsight Optimization to Partially-Observable Markov Decision Processes
Date of Award:
5-2011
Document Type:
Thesis
Degree Name:
Master of Science (MS)
Department:
Computer Science
Advisor/Chair:
Daniel L. Bryce
Abstract
Partially-observable Markov decision processes (POMDPs) are especially good at modeling real-world problems because they allow for sensor and effector uncertainty. Unfortunately, such uncertainty makes solving a POMDP computationally challenging. Traditional approaches, which are based on value iteration, can be slow because they find optimal actions for every possible situation. With the help of the Fast Forward (FF) planner, FF- Replan and FF-Hindsight have shown success in quickly solving fully-observable Markov decision processes (MDPs) by solving classical planning translations of the problem. This thesis extends the concept of problem determination to POMDPs by sampling action observations (similar to how FF-Replan samples action outcomes) and guiding the construction of policy trajectories with a conformant (as opposed to classical) planning heuristic. The resultant planner is called POND-Hindsight.
Checksum
55f942a3fcbd270aebfb8d3d483c78dd
Recommended Citation
Olsen, Alan, "Pond-Hindsight: Applying Hindsight Optimization to Partially-Observable Markov Decision Processes" (2011). All Graduate Theses and Dissertations. 1035.
https://digitalcommons.usu.edu/etd/1035
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Comments
This work is made publicly available electronically on September 29, 2011.