메뉴 건너뛰기




Volumn 3720 LNAI, Issue , 2005, Pages 601-608

Active learning in partially observable Markov decision processes

Author keywords

[No Author keywords available]

Indexed keywords

OPTIMIZING REWARDS; PARTIALLY OBSERVABLE MARKOV DECISION PROCESS (POMDP); POMDP MODELS; POMDP PLANNING PROBLEMS;

EID: 33646410076     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11564096_59     Document Type: Conference Paper
Times cited : (27)

References (10)
  • 2
    • 33646390280 scopus 로고    scopus 로고
    • Resolving perceptual asliasing with noisy sensors
    • Brafman, R. I. and Shani, G. "Resolving perceptual asliasing with noisy sensors". NIPS 2005.
    • NIPS 2005
    • Brafman, R.I.1    Shani, G.2
  • 5
  • 6
    • 33646427325 scopus 로고
    • Learning policies for partially observable environments: Scaling up
    • Brown University
    • Littman, M., Cassandra, A., and Kaelbling,L. "Learning policies for partially observable environments: Scaling up", Technical Report. Brown University, 1995.
    • (1995) Technical Report
    • Littman, M.1    Cassandra, A.2    Kaelbling, L.3
  • 8
    • 84880772945 scopus 로고    scopus 로고
    • Point-based value iteration: An anytime algorithm for POMDPs
    • Pineau, J., Gordon, G. and Thrun, S. "Point-based value iteration: An anytime algorithm for POMDPs". IJCAI. 2003.
    • (2003) IJCAI
    • Pineau, J.1    Gordon, G.2    Thrun, S.3
  • 9
    • 31144457984 scopus 로고    scopus 로고
    • VDCBPI: An approximate scalable algorithm for large scale POMDPs
    • Poupart, P. and Boutilier, C. "VDCBPI: an Approximate Scalable Algorithm for Large Scale POMDPs". NIPS 2005.
    • NIPS 2005
    • Poupart, P.1    Boutilier, C.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.