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Volumn , Issue , 2008, Pages 648-655

Automatic discovery and transfer of MAXQ hierarchies

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; MACHINE LEARNING; TRAJECTORIES; EDUCATION; INFERENCE ENGINES; LEARNING SYSTEMS; REINFORCEMENT; ROBOT LEARNING;

EID: 56449130136     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1390156.1390238     Document Type: Conference Paper
Times cited : (65)

References (14)
  • 1
    • 0036927201 scopus 로고    scopus 로고
    • State Abstraction for Programmable Reinforcement Learning Agents
    • Andre, D., & Russell, S. (2002). State Abstraction for Programmable Reinforcement Learning Agents. AAAI (pp. 119-125).
    • (2002) AAAI , pp. 119-125
    • Andre, D.1    Russell, S.2
  • 2
    • 14344261491 scopus 로고    scopus 로고
    • Using Relative Novelty to Identify Useful Temporal Abstractions in Reinforcement Learning
    • Şimşek, Ö., & Barto, A. (2004). Using Relative Novelty to Identify Useful Temporal Abstractions in Reinforcement Learning. ICML (pp. 751-758).
    • (2004) ICML , pp. 751-758
    • Şimşek, O.1    Barto, A.2
  • 3
    • 0002278788 scopus 로고    scopus 로고
    • Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition
    • Dietterich, T. (2000). Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition. Journal of Artificial Intelligence Research, 13, 227-303.
    • (2000) Journal of Artificial Intelligence Research , vol.13 , pp. 227-303
    • Dietterich, T.1
  • 4
    • 34247204877 scopus 로고    scopus 로고
    • A Hierarchical Approach to Efficient Reinforcement Learning in Deterministic Domains
    • Diuk, C., Littman, M., & Strehl, A. (2006). A Hierarchical Approach to Efficient Reinforcement Learning in Deterministic Domains. AAMAS (pp. 313-319).
    • (2006) AAMAS , pp. 313-319
    • Diuk, C.1    Littman, M.2    Strehl, A.3
  • 5
    • 0013465036 scopus 로고    scopus 로고
    • Discovering Hierarchy in Reinforcement Learning with HEXQ
    • Hengst, B. (2002). Discovering Hierarchy in Reinforcement Learning with HEXQ. ICML (pp. 243-250).
    • (2002) ICML , pp. 243-250
    • Hengst, B.1
  • 6
    • 33750705246 scopus 로고    scopus 로고
    • Causal Graph Based Decomposition of Factored MDPs
    • Jonsson, A., & Barto, A. (2006). Causal Graph Based Decomposition of Factored MDPs. Journal of Machine Learning Research, 7, 2259-2301.
    • (2006) Journal of Machine Learning Research , vol.7 , pp. 2259-2301
    • Jonsson, A.1    Barto, A.2
  • 8
    • 0013465187 scopus 로고    scopus 로고
    • Automatic Discovery of Subgoals in Reinforcement Learning using Diverse Density
    • McGovern, A., & Barto, A. (2001). Automatic Discovery of Subgoals in Reinforcement Learning using Diverse Density. ICML (pp. 361-368).
    • (2001) ICML , pp. 361-368
    • McGovern, A.1    Barto, A.2
  • 10
    • 84945250000 scopus 로고    scopus 로고
    • Q-Cut - Dynamic Discovery of Sub-Goals in Reinforcement Learning
    • Menache, I., Mannor, S., & Shimkin, N. (2001). Q-Cut - Dynamic Discovery of Sub-Goals in Reinforcement Learning. ECML (pp. 295-306).
    • (2001) ECML , pp. 295-306
    • Menache, I.1    Mannor, S.2    Shimkin, N.3
  • 11
    • 14344250461 scopus 로고    scopus 로고
    • PolicyBlocks: An Algorithm for Creating Useful Macro-Actions in Reinforcement Learning
    • Pickett, M., & Barto, A. (2002). PolicyBlocks: An Algorithm for Creating Useful Macro-Actions in Reinforcement Learning. ICML (pp. 506-513).
    • (2002) ICML , pp. 506-513
    • Pickett, M.1    Barto, A.2
  • 12
    • 0033170372 scopus 로고    scopus 로고
    • Between MDPs and Semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning
    • Sutton, R., Precup, D., & Singh, S. (1999). Between MDPs and Semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning. Artificial Intelligence, 112, 181-211.
    • (1999) Artificial Intelligence , vol.112 , pp. 181-211
    • Sutton, R.1    Precup, D.2    Singh, S.3
  • 13
    • 0038145105 scopus 로고    scopus 로고
    • Hierarchical Explanation-Based Reinforcement Learning
    • Tadepalli, P., & Dietterich, T. (1997). Hierarchical Explanation-Based Reinforcement Learning. ICML (pp. 358-366).
    • (1997) ICML , pp. 358-366
    • Tadepalli, P.1    Dietterich, T.2
  • 14
    • 33749882712 scopus 로고
    • Finding Structure in Reinforcement Learning
    • Thrun, S., & Schwartz, A. (1995). Finding Structure in Reinforcement Learning. NIPS (pp. 385-392).
    • (1995) NIPS , pp. 385-392
    • Thrun, S.1    Schwartz, A.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.