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 [BibTeX] [Marc21]
An Attention Mechanism for Deep Q-Networks with Applications in Robotic Pushing
Type of publication: Idiap-RR
Citation: Ewerton_Idiap-RR-03-2021
Number: Idiap-RR-03-2021
Year: 2021
Month: 4
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.
Projects Idiap
Authors Ewerton, Marco
Calinon, Sylvain
Odobez, Jean-Marc
Crossref by Ewerton_ICRAW_2021
Added by: [ADM]
Total mark: 0
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