14–17 Oct 2019
Schedule: Reinforcement Learning sessions
9:00–12:30 Tuesday, 15 October 2019
Location: Blenheim Room - Palace Suite



Average rating:









(5.00, 5 ratings)
Edward Oakes, Peter Schafhalter, and Kristian Hartikainen take a deep dive into Ray, a new distributed execution framework for distributed AI applications developed by machine learning and systems researchers at RISELab, and explore Ray’s API and system architecture and sharing application examples, including several state-of-the-art distributed training, hyperparameter search, and RL algorithms.
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13:30–17:00 Tuesday, 15 October 2019
Location: Windsor Suite

Average rating:









(1.50, 4 ratings)
Sergey Ermolin and Vineet Khare provide a step-by-step overview on how to implement, train, and deploy a reinforcement learning (RL)-based recommender system with real-time multivariate optimization. They show you how leverage RL to implement a recommender system that optimizes an advertisement message that promotes adoption of merchant's services.
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9:40–9:55 Wednesday, 16 October 2019
Location: King's Suite

Average rating:









(4.40, 15 ratings)
If you've ever wondered if you could use AI to inform public policy, join Emily Webber as she combines classic economic methods with AI techniques to train a reinforcement learning agent on decades of randomized control trials. You'll learn about classic philosophical foundations for public policy decision making and how these can be applied to solve the problems that impact the many.
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13:45–14:25 Wednesday, 16 October 2019
Location: Blenheim Room - Palace Suite
Average rating:









(4.67, 3 ratings)
Michael Friedrich and Stefanie Grunwald explore how an algorithm capable of playing Space Invaders can also improve your cloud service's automated scaling mechanism.
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16:00–16:40 Wednesday, 16 October 2019
Location: Blenheim Room - Palace Suite

Average rating:









(4.00, 2 ratings)
Rajib Biswas outlines the application of AI algorithms like generative adversarial networks (GANs) to solve natural language synthesis tasks. Join in to learn how AI can accomplish complex tasks like machine translation, write poetry with style, read a novel, and answer your questions.
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9:20–9:30 Thursday, 17 October 2019
Location: King's Suite

Average rating:









(4.00, 8 ratings)
Reinforcement learning is an advanced machine learning technique that makes short-term decisions while optimizing for a longer-term goal through trial and error. Ian Massingham dives into state-of-the-art techniques in deep reinforcement learning for a variety of use cases.
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11:05–11:45 Thursday, 17 October 2019
Location: Windsor Suite
Average rating:









(4.33, 3 ratings)
In a future of widespread algorithmic pricing, cooperation between algorithms is easier than ever, resulting in coordinated price rises. Rebecca Gu and Cris Lowery explore how a Q-learner algorithm can inadvertently reach a collusive outcome in a virtual marketplace, which industries are likely to be subject to greater restrictions or scrutiny, and what future digital regulation might look like.
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16:00–16:40 Thursday, 17 October 2019
Location: King's Suite - Sandringham
Average rating:









(4.80, 5 ratings)
You're building a high-volume, expensive, robot-driven warehouse. Your robots need to get to the right place quickly, find the right item, and sort it to the right place without colliding with each other, the shelves, or people. But you don't have any robots, and you need to start writing the logic and training them. Paris Buttfield-Addison and Tim Nugent outline how to use a simulation to do it.
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