Next Seminar: Dr. Alexander Vezhnevets on Deep Reinforcement Learning

In the past year, we initiated a very successful series of seminars, Qualcomm-UvA Deep Vision Seminars. The goal is to invite seminal guest speakers to provide talks on the latest advances in the areas of Deep Learning, Computer Vision, and Machine Learning. So far we hosted Prof. I. Kokkinos from INRIA Saclay, Dr. Max Jaderberg from Google Deemind and Dr J. Yosinksi from Geometric Intelligence, Prof. M. Bethge from the University of Tubingen and Dr. J. Sivic from INRIA Paris

In the next Qualcomm-UvA Deep Vision Seminar we will have a talk by Dr. A. Vezhnevets from Google DeepMind. Dr. Veznhevets will give a short intro to deep RL and then dive deeper into their recent work published in ICML.

We invite you all to attend the seminars and join the effort to make Amsterdam a world-renowned research and tech hub on Deep Learning, Computer Vision and Machine Learning. You can find more details about the upcoming presentation below.

Title: FeUdal Networks for Hierarchical Reinforcement learning.

Abstract: Arrival of deep learning has allowed for a significant progress in reinforcement learning (RL). Nevertheless, learning complex behaviours in environments with sparse reward signals, like infamous ATARI Montezuma's revenge, remain a challenge. It is harder still if an environment is partially observable and requires memory. This talk will start with a little introduction into deep RL. The second part will focus on FeUdal Networks (FuNs): a novel network architecture for hierarchical RL. FuN learns to decompose its behaviour into meaningful primitives and then reuse them to more efficiently acquire new, complex behaviours. This allows it to reason on different temporal resolutions and thereby improve long-term credit assignment and memory. FuN dramatically outperform a strong baseline agent on tasks with sparse reward or requiring memorisation. I will demonstrate its performance on a range of tasks from the ATARI suite and also from a 3D DeepMind Lab environment.

Bio: Alexander (Sasha) Vezhnevets is a Senior Research Scientist at Google DeepMind, working on hierarchical reinforcement learning. He got his PhD from ETH Zurich Machine Learning Lab, where he worked on weakly supervised semantic segmentation. He did a post doc at the University of Edinburgh working on object detection and recognition.