Multiple Model-Based Reinforcement Learning

@article{Doya2002MultipleMR,
  title={Multiple Model-Based Reinforcement Learning},
  author={Kenji Doya and Kazuyuki Samejima and Ken-ichi Katagiri and Mitsuo Kawato},
  journal={Neural Computation},
  year={2002},
  volume={14},
  pages={1347-1369}
}
We propose a modular reinforcement learning architecture for nonlinear, nonstationary control tasks, which we call multiple model-based reinforcement learning (MMRL). The basic idea is to decompose a complex task into multiple domains in space and time based on the predictability of the environmental dynamics. The system is composed of multiple modules… CONTINUE READING