A Framework for Reinforcement Learning on Real Robots

William D. Smart, Leslie Pack Kaelbling

Robots are now being used in complex, unstructured environments, performing ever more sophisticated tasks. As the task and environmental complexity increases, the need for effective learning on such systems is becoming more and more apparent. Robot programmers often find it difficult to articulate their knowledge of how to perform a given task in a form suitable for robots to use. Even when they can, the limitations of robot sensors and actuators might render their intuitions less effective. Also, it is often not possible to anticipate (and code for) all environments in which the robot might find itself having to perform a certain task. Therefore, it seems useful to have the robot be able to learn to act, in the hope of overcoming these two difficulties.


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