Learning to Navigate & Learning to leverage disentangled representations for RL

Date: 
Monday, September 17, 2018 - 18:30
Source: 
London Machine Learning
Attendees: 
209
City: 
London

Please note that Photo ID will be required. Please can attendees ensure their meetup profile name includes their full name to ensure entry.

Agenda:
- 18:30: Doors open, pizza, beer, networking
- 19:00: First talk
- 19:45: Break & networking
- 20:00: Second talk
- 20:45: Close

*Learning to Navigate (Piotr Mirowski)

Abstract: Navigation is an important cognitive task that enables humans and animals to traverse, with or without maps, over long distances in the complex world. Such long-range navigation can simultaneously support self-localisation (“I am here”) and a representation of the goal (“I am going there”). For this reason, studying navigation is fundamental to the study and development of artificial intelligence, and trying to replicate navigation in artificial agents can also help neuroscientists understand its biological underpinnings.
This talk will cover our own journey to understand navigation by building deep reinforcement learning agents, starting from learning to control a simple agent that can explore and memorise large 3D mazes, to building agents that can learn to read and write to memory in order to generalise goal acquisition skills to previously unseen environments. I will show how these artificial agents relate to navigation in the real world, both through the study of the emergence of grid cell representations in neural networks -- akin to those found in the mammalian entorhinal cortex -- and by demonstrating that these agents can navigate in Street View-based real world photographic environments.

Bio: Piotr Mirowski is a Senior Research Scientist working in the Deep Learning department at DeepMind, focusing on navigation-related research and in scaling up agents to real world environments. Piotr studied computer science in France (ENSEEIHT, Toulouse) and obtained his PhD in computer science in 2011 at New York University, with a thesis on “Time Series Modeling with Hidden Variables and Gradient-based Algorithms” supervised by Prof. Yann LeCun (Outstanding Dissertation Award, 2011). Prior to joining DeepMind, Piotr worked at Schlumberger Research, at the NYU Comprehensive Epilepsy Center, at Bell Labs and at Microsoft Bing, on problems including geostatistics, geological image processing, epileptic seizure prediction from EEG, WiFi-based geolocalisation, robotics, NLP and search query auto-completion. In his spare time, Piotr performs theatre and improv, with or without robots on the stage, and investigates the use of AI for artistic human and machine-based co-creation.

*Learning and leveraging disentangled representations for RL (Loic Matthey)

Abstract: Deep Reinforcement Learning has shown great success in tackling increasingly more complex tasks, but it still lacks the kind of general and modular reasoning that humans and animals can readily deploy when solving new tasks. A key challenge to overcoming this limitation is learning better state representations for our RL algorithms, to make them more general, useful, interpretable and able to reason about the statistics of the world. I will cover advances in unsupervised representation learning that our team has published over the years, including Beta-VAE, SCAN and more recent works. I will then show how one can leverage such representations for RL and talk about the challenges that arise while doing so.

Bio: Loic Matthey is a Senior Research Scientist at DeepMind, working in the Neuroscience team. He obtained his PhD on Computational Neuroscience and Machine Learning from the Gatsby Unit at UCL, under the supervision of Peter Dayan, working on probabilistic models of visual working memory. Previously, he obtained a MSc in Computer Science and Biocomputing from EPFL in Switzerland. His current research focuses on unsupervised representation learning with a focus on using them for reinforcement learning, and assessing different ways to move towards more general-purpose agents capable of conceptual reasoning.

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