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.

Séries:
Cambridge Lab PhD Summer School
Date:
Haut-parleurs:
John Schulman
Affiliation:
UC Berkeley

Taille: Cambridge Lab PhD Summer School