Using prior knowledge to accelerate online least-squares policy iteration

@article{Busoniu2010UsingPK,
  title={Using prior knowledge to accelerate online least-squares policy iteration},
  author={Lucian Busoniu and Bart De Schutter and Robert Babuska and Damien Ernst},
  journal={2010 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR)},
  year={2010},
  volume={1},
  pages={1-6}
}
Reinforcement learning (RL) is a promising paradigm for learning optimal control. Although RL is generally envisioned as working without any prior knowledge about the system, such knowledge is often available and can be exploited to great advantage. In this paper, we consider prior knowledge about the monotonicity of the control policy with respect to the system states, and we introduce an approach that exploits this type of prior knowledge to accelerate a state-of-the-art RL algorithm called… CONTINUE READING

References

Publications referenced by this paper.
Showing 1-10 of 13 references

and L

  • D. Ernst, P. Geurts
  • Wehenkel, “Tree-based batch mode reinforcement…
  • 2005

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