The Tea Time Talks with Shangtong Zhang (Aug 30, 2018)
This Tea Time Talk features Shangtong Zhang from UAlberta and a talk on Exploration with the Distributional Perspective of Reinforcement Learning
In this presentation, I will talk about how to improve exploration based on the distributional perspective of reinforcement learning. We propose two methods to make use of the distribution of the state-action value function. We use simple MDPs to explain the improved exploration and verify the scalability of our methods in both challenging video games (e.g., 49 Atari games) and physical robot simulators (e.g., 12 Roboschool tasks).
The Tea Time Talks are a series of talks primarily given by the students and faculty studying Artificial Intelligence at the University of Alberta, and provide a comfortable, informal space in which to listen and learn about topics pertaining to machine intelligence and machine learning.
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