Abstract
Linear value function approximation in Markov decision processes (MDPs) has been studied extensively, but there are several challenges when applying such techniques to partially observable MDPs (POMDPs). Furthermore, the system designer often has to choose a set of basis functions. We propose an automatic method to derive a suitable set of basis functions by exploiting the structure of factored models. We experimentally show that our approximation can reduce the solution size by several orders of magnitude in large problems.
Original language | English |
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Title of host publication | AAMAS 2015 - Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems |
Publisher | International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS) |
Pages | 1827-1828 |
Number of pages | 2 |
Volume | 3 |
ISBN (Electronic) | 9781450337717 |
Publication status | Published - 1 Jan 2015 |
Event | AAMAS 2015: 14th International Conference on Autonomous Agents and Multiagent Systems - Istanbul, Turkey Duration: 4 May 2015 → 8 May 2015 Conference number: 14 |
Conference
Conference | AAMAS 2015: 14th International Conference on Autonomous Agents and Multiagent Systems |
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Abbreviated title | AAMAS 2015 |
Country/Territory | Turkey |
City | Istanbul |
Period | 4/05/15 → 8/05/15 |
Keywords
- POMDP
- Value function approximation