Reinforcement Learning of Robotic Motion with Genetic Programming, Simulated Annealing and Self-Organizing Map

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@InProceedings{Wing-KwongWong:2011:TAAI,
  author =       "Wing-Kwong Wong and Hsin-Yu Chen and Chung-You Hsu and 
                 Tsung-Kai Chao",
  title =        "Reinforcement Learning of Robotic Motion with Genetic
                 Programming, Simulated Annealing and Self-Organizing
                 Map",
  booktitle =    "International Conference on Technologies and
                 Applications of Artificial Intelligence (TAAI 2011)",
  year =         "2011",
  month =        "11-13 " # nov,
  pages =        "292--298",
  address =      "Chung-Li, Taiwan",
  size =         "7 pages",
  abstract =     "Reinforcement learning, a sub-area of machine
                 learning, is a method of actively exploring feasible
                 tactics and exploiting already known reward experiences
                 in order to acquire a near-optimal policy. The Q-table
                 of all state-action pairs forms the basis of policy of
                 taking optimal action at each state. But an enormous
                 amount of learning time is required for building the
                 Q-table of considerable size. Moreover, Q-learning can
                 only be applied to problems with discrete state and
                 action spaces. This study proposes a method of genetic
                 programming with simulated annealing to acquire a
                 fairly good program for an agent as a basis for further
                 improvement that adapts to the constraints of an
                 environment. We also propose an implementation of
                 Q-learning to solve problems with continuous state and
                 action spaces using Self-Organising Map (SOM). An
                 experiment was done by simulating a robotic task with
                 the Player/Stage/Gazebo (PSG) simulator. Experimental
                 results showed the proposed approaches were both
                 effective and efficient.",
  keywords =     "genetic algorithms, genetic programming, PSG,
                 Player/Stage/Gazebo, Q-learning, Q-table, SOM, machine
                 learning, optimal action, reinforcement learning,
                 robotic motion, self-organising map, simulated
                 annealing, control engineering computing, learning
                 (artificial intelligence), robots, self-organising
                 feature maps, simulated annealing",
  DOI =          "doi:10.1109/TAAI.2011.57",
  notes =        "Also known as \cite{6120760}",
}

Genetic Programming entries for Wing-Kwong Wong Hsin-Yu Chen Chung-You Hsu Tsung-Kai Chao

Citations