RE•WORK Deep Learning Summit

Deep Reinforcement Learning (DRL) has made strong progress in many tasks that are traditionally considered to be difficult, such as complete information games, navigation, architecture search, etc. Although the basic principle of DRL is quite simple and straightforward, to make it work often requires substantial efforts, compared to traditional supervised training. In this talk, we introduce our recent open-sourced ELF platform: efficient, lightweight and flexible frameworks to facilitate DRL research. We show the scalability of our platforms by reproducing and open sourcing AlphaGoZero/AlphaZero framework using 2000 GPUs and 1.5 weeks, achieving super-human performance of Go AI that beats 4 top-30 professional players with 20-0. We also show usability of our platform by training agents in real-time strategy games with only a small amount of resource. The trained agent develops interesting tactics and is able to beat rule-based AIs by a large margin. On the environment side, we propose House3D that makes multi-room navigation easy with fast frame rate. With House3D, we show that model-based agent that plans ahead with uncertain information navigate in unseen environments more successfully.