Towards Generalization and Simplicity in Continuous Control

@inproceedings{Rajeswaran2017TowardsGA,
  title={Towards Generalization and Simplicity in Continuous Control},
  author={Aravind Rajeswaran and Kendall Lowrey and Emanuel Todorov and Sham M. Kakade},
  booktitle={NIPS},
  year={2017}
}
This work shows that policies with simple linear and RBF parameterizations can be trained to solve a variety of widely studied continuous control tasks, including the OpenAI gym benchmarks. The performance of these trained policies are competitive with state of the art results, obtained with more elaborate parameterizations such as fully connected neural networks. Furthermore, the standard training and testing scenarios for these tasks are shown to be very limited and prone to overfitting, thus… CONTINUE READING
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