CS671: Probabilistic Agent Learning (Spring 2019)

[Image from https://deepmind.com/blog/reinforcement-learning-unsupervised-auxiliary-tasks/]

Sungjin Ahn

This seminar course covers recent advances in deep & probabilistic generative approaches for agent learning. This includes (1) fundamental advances in probabilistic generative modelling, mostly based on variational inference, and (2) its applications to designing various cognitive abilities such as imagination, exploration, and planning for a probabilistic agent. This includes topics such as structured latent representation learning, spatiotemporal models for future imagination, reinforcement learning as probabilistic inference, exploration/intrinsic motivation, hierarchical option discovery, meta/continual learning, model-based learning, and so on. 

Time and Location 
Thursday 3:20pm - 6:20pm
Busch SEC 206