Adaptive Step-Size for Policy Gradient Methods

NeurIPS 2013 Matteo PirottaMarcello RestelliLuca Bascetta

In the last decade, policy gradient methods have significantly grown in popularity in the reinforcement--learning field. In particular, they have been largely employed in motor control and robotic applications, thanks to their ability to cope with continuous state and action domains and partial observable problems... (read more)

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