Reinforcement learning (RL) is a general-purpose machine learning framework, which considers an agent that makes sequential decisions in an environment to maximize its reward. Deep reinforcement learning (DRL) approaches have made significant advances in the recent years by allowing the agent to learn a policy directly from raw observations. In this talk, I will first present how deep learning can be used to further improve RL agent's ability to perform planning by predicting the future and generalize to new environments and tasks. In addition, I will present AlphaStar which is the first AI to defeat a top professional player in the game of Starcraft, one of the most challenging Real-Time Strategy (RTS) games. Specifically, I will show how such complex strategies can emerge through a distributed multi-agent RL algorithm. Finally, I will discuss remaining challenges towards artificial general intelligence (AGI).

Junhyuk Oh is a research scientist at DeepMind. He received his Ph.D. from Computer Science and Engineering at the University of Michigan in 2018, co-advised by Prof. Honglak Lee and Prof. Satinder Singh, and B.S. from Computer Science and Engineering at Seoul National University in 2014. His research focuses on deep reinforcement learning problems such as dealing with partial observability, generalization, planning, and multi-agent reinforcement learning. His work was featured at MIT Technology Review and Daily Mail. He served as a co-organizer of NIPS 2017 symposium on deep reinforcement learning, ICML 2018 workshop on exploration in reinforcement learning, and ICLR 2019 workshop on structures and priors in reinforcement learning.