Off-Policy Reinforcement Learning for Robotics (original) (raw)

Nowadays, industrial processes are vastly automated by means of robotic manipulators. In some cases, robots occupy a large fraction of the production line, performing a rich range of tasks. In contrast to their tireless ability to repeatedly perform the same tasks with millimetric precision, current robotics exhibits low adaptability to new scenarios. This lack of adaptability in many cases hinders a closer human-robot interaction; furthermore, when one needs to apply some change to the production line, the robots need to be reconfigured by highly-qualified figures. Machine learning and, more particularly, reinforcement learning hold the promise to provide automated systems that can adapt to new situations and learn new tasks. Despite the overwhelming progress in recent years in the field, the vast majority of reinforcement learning is not directly applicable to real robotics. State-of-the-art reinforcement learning algorithms require intensive interaction with the environment and a...

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