Presented By O’Reilly and Intel AI
Put AI to work
8-9 Oct 2018: Training
9-11 Oct 2018: Tutorials & Conference
London, UK

Schedule: Reinforcement Learning sessions

13:30–17:00 Tuesday, 9 October 2018
Location: Blenheim Room - Palace Suite
Richard Liaw (UC Berkeley RISELab), Eric Liang (UC Berkeley RISELab)
Ion Stoica, Robert Nishihara, Richard Liaw, Eric Liang, and Philipp Moritz lead a deep dive into Ray, a new distributed execution framework for reinforcement learning applications, walking you through Ray's API and system architecture and sharing application examples, including several state-of-the art RL algorithms. Read more.
11:55–12:35 Wednesday, 10 October 2018
Interacting with AI
Location: King's Suite - Sandringham
Danny Lange (Unity Technologies)
Danny Lange discusses the role of intelligence in biological evolution and learning and demonstrates why a game engine is the perfect virtual biodome for AI’s evolution. You'll discover how the scale and speed of simulations is changing the game of AI while learning about new developments in reinforcement learning. Read more.
16:50–17:30 Wednesday, 10 October 2018
Location: Westminster Suite
Gal Novik (Intel AI)
Gal Novik offers an overview of Reinforcement Learning Coach, an open source Python library that models the interaction between an agent and an environment in a modular way, making it easy for researchers to implement new reinforcement learning algorithms and for data scientists to integrate additional simulation environments modeling their business problems. Read more.
16:00–16:40 Thursday, 11 October 2018
Implementing AI, Models and Methods
Location: King's Suite - Balmoral
Dr. Sid J Reddy (Conversica)
Sid Reddy shows you how to avoid the hype and decide which use cases are the best for deep reinforcement learning. You'll explore the Markov decision process with conversational AI and learn how to set up the environment, states, agent actions, transition probabilities, reward functions, and end states. You'll also discover when to use end-to-end reinforcement learning. Read more.