|
|
|
|
| LEADER |
01848 am a22002533u 4500 |
| 001 |
81767 |
| 042 |
|
|
|a dc
|
| 100 |
1 |
0 |
|a Geramifard, Alborz
|e author
|
| 100 |
1 |
0 |
|a Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
|e contributor
|
| 100 |
1 |
0 |
|a Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
|e contributor
|
| 100 |
1 |
0 |
|a Ure, Nazim Kemal
|e contributor
|
| 100 |
1 |
0 |
|a Geramifard, Alborz
|e contributor
|
| 100 |
1 |
0 |
|a Chowdhary, Girish
|e contributor
|
| 100 |
1 |
0 |
|a How, Jonathan P.
|e contributor
|
| 700 |
1 |
0 |
|a Chowdhary, Girish
|e author
|
| 700 |
1 |
0 |
|a How, Jonathan P.
|e author
|
| 700 |
1 |
0 |
|a Ure, Nazim Kemal
|e author
|
| 245 |
0 |
0 |
|a Adaptive Planning for Markov Decision Processes with Uncertain Transition Models via Incremental Feature Dependency Discovery
|
| 260 |
|
|
|b Springer-Verlag,
|c 2013-10-25T13:18:47Z.
|
| 856 |
|
|
|z Get fulltext
|u http://hdl.handle.net/1721.1/81767
|
| 520 |
|
|
|a Solving large scale sequential decision making problems without prior knowledge of the state transition model is a key problem in the planning literature. One approach to tackle this problem is to learn the state transition model online using limited observed measurements. We present an adaptive function approximator (incremental Feature Dependency Discovery (iFDD)) that grows the set of features online to approximately represent the transition model. The approach leverages existing feature-dependencies to build a sparse representation of the state transition model. Theoretical analysis and numerical simulations in domains with state space sizes varying from thousands to millions are used to illustrate the benefit of using iFDD for incrementally building transition models in a planning framework.
|
| 546 |
|
|
|a en_US
|
| 655 |
7 |
|
|a Article
|
| 773 |
|
|
|t Machine Learning and Knowledge Discovery in Databases
|
