Parallel Recombinative Reinforcement Learning

@inproceedings{Blekas2012ParallelRR,
  title={Parallel Recombinative Reinforcement Learning},
  author={Konstantinos Blekas and Andreas StafylopatisComputer},
  year={2012}
}
A technique is presented which is suitable for function optimization in high-dimensional binary domains. The method allows an eecient parallel implementation and is based on the combination of genetic algorithms and reinforcement learning schemes. More speciically, a population of probability vectors is considered, each member corresponding to a reinforcement learning optimizer. Each probability vector represents the adaptable parameters of a team of stochastic units whose binary outputs… CONTINUE READING

References

Publications referenced by this paper.
SHOWING 1-10 OF 18 REFERENCES

A Reinforcement Learning Algorithm for Networks of Units with Two Stochastic Levels

  • D. Kontoravdis, A. Likas, A. Stafylopatis
  • Proceedings ICANN-92,
  • 1992
2 Excerpts

A note on Boltzmann Tournament Selection for Genetic Algorithms and Population-oriented Simulated Annealing

  • D. E. Goldberg
  • Complex Systems,
  • 1990
1 Excerpt

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