3D Control and Reasoning without a Supercomputer

Edward Beeching
Jilles Dibangoye
Olivier Simonin
Christian Wolf
CITI, INRIA CHROMA, INSA Lyon
CITI, INRIA CHROMA, INSA Lyon
CITI, INRIA CHROMA, INSA Lyon
LIRIS, CNRS, INSA Lyon

Published at ICPR, 2020

[Paper]
[Code]
[Talk (TODO)]
[Slides (TODO)]


Abstract

An important goal of research in Deep Reinforcement Learning in mobile robotics is to train agents capable of solving complex tasks, which require a high level of scene understanding and reasoning from an egocentric perspective. When trained from simulations, optimal environments should satisfy a currently unobtainable combination of high-fidelity photographic observations, massive amounts of different environment configurations and fast simulation speeds. In this paper we argue that research on training agents capable of complex reasoning can be simplified by decoupling from the requirement of high fidelity photographic observations. We present a suite of tasks requiring complex reasoning and exploration in continuous, partially observable 3D environments. The objective is to provide challenging scenarios and a robust baseline agent architecture that can be trained on mid-range consumer hardware in under 24h. Our scenarios combine two key advantages: (i) they are based on a simple but highly efficient 3D environment (ViZDoom) which allows high speed simulation (12000fps); (ii) the scenarios provide the user with a range of difficulty settings, in order to identify the limitations of current state of the art algorithms and network architectures. We aim to increase accessibility to the field of Deep-RL by providing baselines for challenging scenarios where new ideas can be iterated on quickly. We argue that the community should be able to address challenging problems in reasoning of mobile agents without the need for a large compute infrastructure.


Presentation (TODO)



Paper and Bibtex

[Paper]

Citation
 
Beeching, E., Dibangoye, J., Simonin, O., and Wolf, C., 2020. Deep Reinforcement Learning on a Budget: 3D Control and Reasoning Without a Supercomputer. In proceedings of the International Conference on Pattern Recognition

[Bibtex]
@inproceedings{beeching2020baselines,
  title={Deep Reinforcement Learning on a Budget: 3D Control and Reasoning Without a
    Supercomputer.
  },
  author={Beeching, Edward and Dibangoye, Jilles and 
          Simonin, Olivier and Wolf, Christian}
  booktitle={International Conference on Pattern Recognition},
  year={2020}}
                


Acknowledgements

This work was funded by grant Deepvision (ANR-15-CE23-0029, STPGP479356-15), a joint French/Canadian call by ANR \& NSERC. We gratefully acknowledge support from the CNRS/IN2P3 Computing Center (Lyon - France) for providing computing and data-processing resources needed for this work.
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