Policy Gradient Methods: Tutorial and New Frontiers

In this tutorial we discuss several recent advances in deep reinforcement learning involving policy gradient methods. These methods have shown significant success in a wide range of domains, including continuous-action domains such as manipulation, locomotion, and flight. They have also achieved the state of the art in discrete action domains such as Atari. We will provide a unifying overview of a variety of different policy gradient methods, and we will also discuss the formalism of stochastic computation graphs for computing gradients of expectations.

专题：
Cambridge Lab PhD Summer School
日期：
演讲者：
John Schulman
所属机构：
UC Berkeley

系列： Cambridge Lab PhD Summer School