Junhyuk Oh

PhD Researcher
University of Michigan

Task Generalization with Deep Reinforcement Learning

The ability to generalize over new tasks is an important research direction in order to build scalable reinforcement learning agents. In this talk, I will discuss how to easily train an agent to generalize to previously unseen tasks in a zero-shot fashion.

Junhyuk Oh is a PhD candidate at the University of Michigan, advised by Professor Honglak Lee and Professor Satinder Singh. His research focuses on deep reinforcement learning problems such as action-conditional prediction, dealing with partial observability, generalization, and planning. His work was featured at MIT Technology Review and Daily Mail. He has served as a co-organizer and a program committee of NIPS deep reinforcement learning symposium and workshop. He also interned at DeepMind and Microsoft Research.

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