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)

PDF Abstract


No code implementations yet. Submit your code now

Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper

🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet