Description: A high-level overview of Deep Reinforcement Learning (DRL) is presented, how it is useful, and how it applies to the goal of achieving Artificial General Intelligence (AGI). We discuss the major algorithms involved in Deep Reinforcement Learning including value-based approaches such as Deep Q-Learning, Policy Gradient approaches such as Proximal Policy Optimization (PPO), and hybrid Actor-Critic methods such as Deep Deterministic Policy Gradients (DDPG). We briefly show how DRL systems can be built using PyTorch with the Unity ML Agents Toolkit.
Presenter(s): Dr. Wes Brewer - PET GDIT
Location: Webcast
Date & Time: February 11, 2020, 3:00p - 4:30p ET
Additional Notes: Part of the HPDA Seminar Series (2020).
Distribution Statement C. Distribution a authorized to U.S. Government Agencies and their contractors, Administrative or Operational Use, 11 February 2020. Other requests for this document shall be referred to the High Performance Computing Modernization Office, 3909 Halls Ferry Road, Vicksburg, MS 39180.