Basis Function Adaptation in Temporal Difference Reinforcement Learning

@article{Menache2005BasisFA,
  title={Basis Function Adaptation in Temporal Difference Reinforcement Learning},
  author={Ishai Menache and Shie Mannor and Nahum Shimkin},
  journal={Annals OR},
  year={2005},
  volume={134},
  pages={215-238}
}
Reinforcement Learning (RL) is an approach for solving complex multistage decision problems that fall under the general framework of Markov Decision Problems (MDPs), with possibly unknown parameters. Function approximation is essential for problems with a large state space, as it facilitates compact representation and enables generalization. Linear approximation architectures (where the adjustable parameters are the weights of pre-fixed basis functions) have recently gained prominence due to… CONTINUE READING
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