Safe Policy Search for Lifelong Reinforcement Learning with Sublinear Regret
published: Sept. 27, 2015, recorded: July 2015, views: 1635
Download slides: icml2015_eaton_lifelong_reinforcement_learning_01.pdf (845.2 KB)
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lifelong reinforcement learning provides a promising framework for developing versatile agents that can accumulate knowledge over a lifetime of experience and rapidly learn new tasks by building upon prior knowledge. However, current lifelong learning methods exhibit non-vanishing regret as the amount of experience increases, and include limitations that can lead to suboptimal or unsafe control policies. To address these issues, we develop a lifelong policy gradient learner that operates in an adversarial setting to learn multiple tasks online while enforcing safety constraints on the learned policies. We demonstrate, for the first time, sublinear regret for lifelong policy search, and validate our algorithm on several benchmark dynamical systems and an application to quadrotor control.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !