Training Reinforcement Neurocontrollers Using the Polytope Algorithm

@article{Likas1998TrainingRN,
  title={Training Reinforcement Neurocontrollers Using the Polytope Algorithm},
  author={Aristidis Likas and Isaac E. Lagaris},
  journal={Neural Processing Letters},
  year={1998},
  volume={9},
  pages={119-127}
}
  • Aristidis Likas, Isaac E. Lagaris
  • Published in Neural Processing Letters 1998
  • Computer Science, Mathematics
  • A new training algorithm is presented for delayed reinforcement learning problems that does not assume the existence of a critic model and employs the polytope optimization algorithm to adjust the weights of the action network so that a simple direct measure of the training performance is maximized. Experimental results from the application of the method to the pole balancing problem indicate improved training performance compared with critic-based and genetic reinforcement approaches. 

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