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  • 1
    Electronic Resource
    Electronic Resource
    The Parti-game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-spaces (1995)
    Springer
    Machine learning 21 (1995), S. 199-233 
    Details
    ISSN: 0885-6125
    Keywords: Reinforcement Learning ; Curse of Dimensionality ; Learning Control ; Robotics ; kd-trees
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Parti-game is a new algorithm for learning feasible trajectories to goal regions in high dimensional continuous state-spaces. In high dimensions it is essential that neither planning nor exploration occurs uniformly over a state-space. Parti-game maintains a decision-tree partitioning of state-space and applies techniques from game-theory and computational geometry to efficiently and adaptively concentrate high resolution only on critical areas. The current version of the algorithm is designed to find feasible paths or trajectories to goal regions in high dimensional spaces. Future versions will be designed to find a solution that optimizes a real-valued criterion. Many simulated problems have been tested, ranging from two-dimensional to nine-dimensional state-spaces, including mazes, path planning, non-linear dynamics, and planar snake robots in restricted spaces. In all cases, a good solution is found in less than ten trials and a few minutes.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1022656217772
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    Paper (German National Licenses)
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  • 2
    Electronic Resource
    Electronic Resource
    The parti-game algorithm for variable resolution reinforcement learning in multidimensional state-spaces (1995)
    Springer
    Machine learning 21 (1995), S. 199-233 
    Details
    ISSN: 0885-6125
    Keywords: Reinforcement Learning ; Curse of Dimensionality ; Learning Control ; Robotics ; kd-trees
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Parti-game is a new algorithm for learning feasible trajectories to goal regions in high dimensional continuous state-spaces. In high dimensions it is essential that neither planning nor exploration occurs uniformly over a state-space. Parti-game maintains a decision-tree partitioning of state-space and applies techniques from game-theory and computational geometry to efficiently and adaptively concentrate high resolution only on critical areas. The current version of the algorithm is designed to find feasible paths or trajectories to goal regions in high dimensional spaces. Future versions will be designed to find a solution that optimizes a real-valued criterion. Many simulated problems have been rested, ranging from two-dimensional to nine-dimensional state-spaces, including mazes, path planning, non-linear dynamics, and planar snake robots in restricted spaces. In all cases, a good solution is found in less than ten trials and a few minutes.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00993591
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    Paper (German National Licenses)
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  • 3
    Electronic Resource
    Electronic Resource
    Locally Weighted Learning (1997)
    Springer
    Artificial intelligence review 11 (1997), S. 11-73 
    Details
    ISSN: 1573-7462
    Keywords: locally weighted regression ; LOESS ; LWR ; lazy learning ; memory-based learning ; least commitment learning ; distance functions ; smoothing parameters ; weighting functions ; global tuning ; local tuning ; interference
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract This paper surveys locally weighted learning, a form of lazy learning and memory-based learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, assessing predictions, handling noisy data and outliers, improving the quality of predictions by tuning fit parameters, interference between old and new data, implementing locally weighted learning efficiently, and applications of locally weighted learning. A companion paper surveys how locally weighted learning can be used in robot learning and control.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1006559212014
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  • 4
    Electronic Resource
    Electronic Resource
    Locally Weighted Learning for Control (1997)
    Springer
    Artificial intelligence review 11 (1997), S. 75-113 
    Details
    ISSN: 1573-7462
    Keywords: locally weighted regression ; LOESS ; LWR ; lazy learning ; memory-based learning ; least commitment learning ; forward models ; inverse models ; linear quadratic regulation (LQR) ; shifting setpoint algorithm ; dynamic programming
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Lazy learning methods provide useful representations and training algorithms for learning about complex phenomena during autonomous adaptive control of complex systems. This paper surveys ways in which locally weighted learning, a type of lazy learning, has been applied by us to control tasks. We explain various forms that control tasks can take, and how this affects the choice of learning paradigm. The discussion section explores the interesting impact that explicitly remembering all previous experiences has on the problem of learning to control.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1006511328852
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