%Aigaion2 BibTeX export from Idiap Publications
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@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}
}