A Laplacian Framework for Option Discovery in Reinforcement Learning

@inproceedings{Machado2017ALF,
  title={A Laplacian Framework for Option Discovery in Reinforcement Learning},
  author={Marlos C. Machado and Marc G. Bellemare and Michael H. Bowling},
  booktitle={ICML},
  year={2017}
}
Representation learning and option discovery are two of the biggest challenges in reinforcement learning (RL). Proto-RL is a well known approach for representation learning in MDPs. The representations learned with this framework are called proto-value functions (PVFs). In this paper we address the option discovery problem by showing how PVFs implicitly define options. We do it by introducing eigenpurposes, intrinsic reward functions derived from the learned representations. The options… CONTINUE READING
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