Many-objective stochastic path finding using reinforcement learning

@article{Tozer2017ManyobjectiveSP,
  title={Many-objective stochastic path finding using reinforcement learning},
  author={Bentz Tozer and Thomas A. Mazzuchi and Shahram Sarkani},
  journal={Expert Syst. Appl.},
  year={2017},
  volume={72},
  pages={371-382}
}
In this paper, we investigate solutions to path finding problems with many conflicting objectives, and introduce a new model-free many objective reinforcement learning algorithm, called Voting Q-learning, that is capable of finding a set of optimal policies in an initially unknown, stochastic environment with several conflicting objectives. Current methods for solving this type of problem rely on Pareto dominance to determine which actions are optimal, which decreases in effectiveness as the… CONTINUE READING
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