Training Reinforcement Neurocontrollers Using the Polytope Algorithm
<|reference_start|>Training Reinforcement Neurocontrollers Using the Polytope Algorithm: 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.<|reference_end|>
@article{likas1998training,
    title={Training Reinforcement Neurocontrollers Using the Polytope Algorithm},
    author={A. Likas and I. E. Lagaris},
    journal={arXiv preprint arXiv:cs/9812002},
    year={1998},
    number={Preprint, Dept. of Computer Science, Univ. of Ioannina, 1996},
    archivePrefix={arXiv},
    eprint={cs/9812002},
    primaryClass={cs.NE}
}