Corpus ID: 17333616

Information Theoretically Aided Reinforcement Learning for Embodied Agents

@article{Montfar2016InformationTA,
  title={Information Theoretically Aided Reinforcement Learning for Embodied Agents},
  author={Guido Mont{\'u}far and K. Zahedi and N. Ay},
  journal={ArXiv},
  year={2016},
  volume={abs/1605.09735}
}

Guido Montúfar, K. Zahedi, N. Ay

Published 2016

Computer Science, Mathematics

ArXiv

Reinforcement learning for embodied agents is a challenging problem. The accumulated reward to be optimized is often a very rugged function, and gradient methods are impaired by many local optimizers. We demonstrate, in an experimental setting, that incorporating an intrinsic reward can smoothen the optimization landscape while preserving the global optimizers of interest. We show that policy gradient optimization for locomotion in a complex morphology is significantly improved when… CONTINUE READING

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