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GTC Silicon Valley-2019 ID:S9786:Doing More with More: Recent Achievements in Large-Scale Deep Reinforcement Learning

Adam Stooke(UC Berkeley)
Learn about recent achievements in deep reinforcement learning (RL) with a focus on using large-scale compute resources. We'll cover basic algorithms, discuss development of RL agents for playing Atari games, and provide a chronology of implementations leveraging increasing amounts of hardware to achieve better results faster. We will describe large-scale RL projects in which learned agents surpassed human-level performance in the challenging games of Go, Quake III, and Dota2. For each project, we'll discuss the distinct hardware used and the techniques we developed to scale up the algorithm. These projects demonstrate a range of strategies for harnessing many CPUs and GPUs. We'll outline continued research in this area and explore the potential for more exciting results around the corner.

View the slides (pdf)