Back to Search
Start Over
Partially observable Markov decision processes with reward information: basic ideas and models
- Source :
- IEEE Transactions on Automatic Control. April, 2007, Vol. 52 Issue 4, p677, 5 p.
- Publication Year :
- 2007
-
Abstract
- In a partially observable Markov decision process (POMDP), if the reward can be observed at each step, then the observed reward history contains information on the unknown state. This information, in addition to the information contained in the observation history, can be used to update the state probability distribution. The policy thus obtained is called a reward-information policy (RI-policy); an optimal RI-policy performs no worse than any normal optimal policy depending only on the observation history. The above observation leads to four different problem-formulations for POMDPs depending on whether the reward function is known and whether the reward at each step is observable. This exploratory work may attract attention to these interesting problems. Index Terms--Partially observable Markov decision process (POMDP), reward-information policy. Digital Object Identifier 10.1109/TAC.2007.894520
- Subjects :
- Markov processes -- Analysis
Subjects
Details
- Language :
- English
- ISSN :
- 00189286
- Volume :
- 52
- Issue :
- 4
- Database :
- Gale General OneFile
- Journal :
- IEEE Transactions on Automatic Control
- Publication Type :
- Academic Journal
- Accession number :
- edsgcl.163064003