Hybrid MDP based integrated hierarchical Q-learning View Full Text


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Article Info

DATE

2011-11

AUTHORS

ChunLin Chen, DaoYi Dong, Han-Xiong Li, Tzyh-Jong Tarn

ABSTRACT

As a widely used reinforcement learning method, Q-learning is bedeviled by the curse of dimensionality: The computational complexity grows dramatically with the size of state-action space. To combat this difficulty, an integrated hierarchical Q-learning framework is proposed based on the hybrid Markov decision process (MDP) using temporal abstraction instead of the simple MDP. The learning process is naturally organized into multiple levels of learning, e.g., quantitative (lower) level and qualitative (upper) level, which are modeled as MDP and semi-MDP (SMDP), respectively. This hierarchical control architecture constitutes a hybrid MDP as the model of hierarchical Q-learning, which bridges the two levels of learning. The proposed hierarchical Q-learning can scale up very well and speed up learning with the upper level learning process. Hence this approach is an effective integral learning and control scheme for complex problems. Several experiments are carried out using a puzzle problem in a gridworld environment and a navigation control problem for a mobile robot. The experimental results demonstrate the effectiveness and efficiency of the proposed approach. More... »

PAGES

2279

References to SciGraph publications

  • 2006. Grey Reinforcement Learning for Incomplete Information Processing in THEORY AND APPLICATIONS OF MODELS OF COMPUTATION
  • 2009-11. Advances in automation and control research in China in SCIENCE IN CHINA SERIES F INFORMATION SCIENCES
  • 1988-08. Learning to predict by the methods of temporal differences in MACHINE LEARNING
  • 2002-11. Kernel-Based Reinforcement Learning in MACHINE LEARNING
  • 2009-12. Q-learning based heterogenous network self-optimization for reconfigurable network with CPC assistance in SCIENCE IN CHINA SERIES F INFORMATION SCIENCES
  • 2003-01. Recent Advances in Hierarchical Reinforcement Learning in DISCRETE EVENT DYNAMIC SYSTEMS
  • 1992-05. Q-learning in MACHINE LEARNING
  • 1996-03. Average reward reinforcement learning: Foundations, algorithms, and empirical results in MACHINE LEARNING
  • 1996-03. Incremental multi-step Q-learning in MACHINE LEARNING
  • Identifiers

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    http://scigraph.springernature.com/pub.10.1007/s11432-011-4332-6

    DOI

    http://dx.doi.org/10.1007/s11432-011-4332-6

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    https://app.dimensions.ai/details/publication/pub.1026110937


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