%Aigaion2 BibTeX export from Idiap Publications
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@INPROCEEDINGS{Ewerton_ICRAW_2021,
                      author = {Ewerton, Marco and Calinon, Sylvain and Odobez, Jean-Marc},
                    projects = {Idiap},
                       title = {An Attention Mechanism for Deep Q-Networks with Applications in Robotic Pushing},
                   booktitle = {Proc. of Workshop on Emerging paradigms for robotic manipulation: from the lab to the productive world, ICRA},
                        year = {2021},
                    crossref = {Ewerton_Idiap-RR-03-2021}
}



crossreferenced publications: 
@TECHREPORT{Ewerton_Idiap-RR-03-2021,
                      author = {Ewerton, Marco and Calinon, Sylvain and Odobez, Jean-Marc},
                    projects = {Idiap},
                       month = {4},
                       title = {An Attention Mechanism for Deep Q-Networks with Applications in Robotic Pushing},
                        type = {Idiap-RR},
                      number = {Idiap-RR-03-2021},
                        year = {2021},
                 institution = {Idiap},
                    abstract = {Humans effortlessly solve push tasks in everyday life but unlocking these capabilities remains a research challenge in robotics. Physical models are often inaccurate or unattainable. State-of-the-art data-driven approaches learn to compensate for these inaccuracies or get rid of the approximated physical models altogether. Nevertheless, data-driven approaches such as Deep Q-Networks (DQNs) get frequently stuck in local optima in large state-action spaces. We propose an attention mechanism for DQNs to improve their sampling efficiency and demonstrate in simulation experiments with a UR5 robot arm that such a mechanism helps the DQN learn faster and achieve higher performance in a push task involving objects with unknown dynamics.},
                         pdf = {https://publications.idiap.ch/attachments/reports/2021/Ewerton_Idiap-RR-03-2021.pdf}
}