Learning-Based Task Allocation in Decentralized Multirobot Systems

  • Poj Tangamchit
  • John M. Dolan
  • Pradeep K. Khosla


This paper presents an efficient decentralized multirobot system through the use of reinforcement learning coupled with heuristics to accelerate the learning process. Our heuristic functions are not reinforcement signals, but are biases to the learning space that help accelerate the exploration process and have no effect on the robot’s knowledge. Two kinds of heuristics are used: local and global. The local heuristic is derived from the local knowledge of the robot, whereas the global heuristic is derived from the data obtained from other robots. Both heuristics can help accelerating the learning process.


Reinforcement Learning Local Knowledge Travel Salesman Problem Travel Salesman Problem Reinforcement Signal 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Tokyo 2000

Authors and Affiliations

  • Poj Tangamchit
    • 1
  • John M. Dolan
    • 2
  • Pradeep K. Khosla
    • 1
    • 2
  1. 1.Dept. of Electrical and Computer EngineeringCarnegie Mellon Univ.PittsburghUSA
  2. 2.The Robotics InstituteCarnegie Mellon Univ.PittsburghUSA

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