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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
    Prioritized Sweeping: Reinforcement Learning with Less Data and Less Time (1993)
    Springer
    Machine learning 13 (1993), S. 103-130 
    Details
    ISSN: 0885-6125
    Keywords: Memory-based learning ; learning control ; reinforcement learning ; temporal differencing ; asynchronous dynamic programming ; heuristic search ; prioritized sweeping
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We present a new algorithm, prioritized sweeping, for efficient prediction and control of stochastic Markov systems. Incremental learning methods such as temporal differencing and Q-learning have real-time performance. Classical methods are slower, but more accurate, because they make full use of the observations. Prioritized sweeping aims for the best of both worlds. It uses all previous experiences both to prioritize important dynamic programming sweeps and to guide the exploration of state-space. We compare prioritized sweeping with other reinforcement learning schemes for a number of different stochastic optimal control problems. It successfully solves large state-space real-time problems with which other methods have difficulty.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1022635613229
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    Paper (German National Licenses)
    Fulltext
  • 3
    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
    Location Call Number Expected Availability
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    Paper (German National Licenses)
    Fulltext
  • 4
    Electronic Resource
    Electronic Resource
    Prioritized sweeping: Reinforcement learning with less data and less time (1993)
    Springer
    Machine learning 13 (1993), S. 103-130 
    Details
    ISSN: 0885-6125
    Keywords: Memory-based learning ; learning control ; reinforcement learning ; temporal differencing ; asynchronous dynamic programming ; heuristic search ; prioritized sweeping
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We present a new algorithm,prioritized sweeping, for efficient prediction and control of stochastic Markov systems. Incremental learning methods such as temporal differencing and Q-learning have real-time performance. Classical methods are slower, but more accurate, because they make full use of the observations. Prioritized sweeping aims for the best of both worlds. It uses all previous experiences both to prioritize important dynamic programming sweeps and to guide the exploration of state-space. We compare prioritized sweeping with other reinforcement learning schemes for a number of different stochastic optimal control problems. It successfully solves large state-space real-time problems with which other methods have difficulty.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00993104
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    Paper (German National Licenses)
    Fulltext
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