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From Pixels to Torques: Policy Learning using Deep Dynamical Convolutional Neural Networks (DDCNN)

Data-efficient learning in continuous state-action spaces using high-dimensional observations remains a elusive challenge in developing fully autonomous systems. An instance of this challenge is the pixels to torques problem, which identifies key elements of an autonomous agent: autonomous thinking and decision making using sensor measurements only, learning from mistakes, and applying past experiences to novel situations. In this research, we introduce a deep dynamical convolutional model, able to learn complex non-linear dynamics and do long-term predictions. Compared to state-of-the-art reinforcement learning methods for continuous state and action space problems, our approach is solid and efficient as it is model-based, is scalable to high-dimensional state spaces, learns quickly, and is a major step towards fully autonomous learning from pixels to torques.

Bibtex

@article{assael2015data,
  title={Data-Efficient Learning of Feedback Policies from Image Pixels using Deep Dynamical Models},
  author={Assael, J.-A. M and Wahlstr{\"o}m, N. and Sch{\"o}n, T. B. and Deisenroth, M. P.},
  journal={NIPS Deep Reinforcement Learning Workshop},
  year={2015}
}

License

Copyright (C) 2015 John-Alexander M. Assael, Marc P. Deisenroth

The MIT License (MIT)

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