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Put AI to work
Sep 4-5, 2018: Training
Sep 5-7, 2018: Tutorials & Conference
San Francisco, CA

Schedule: Reinforcement Learning sessions

1:30pm-5:00pm Wednesday, September 5, 2018
Implementing AI
Location: Continental 7/8
Robert Nishihara (UC Berkeley), Philipp Moritz (UC Berkeley), Ion Stoica (UC Berkeley)
Average rating: ***..
(3.00, 3 ratings)
Ray is a new distributed execution framework for reinforcement learning applications. Ion Stoica, Robert Nishihara, and Philipp Moritz lead a deep dive into Ray, walking you through its API and system architecture and sharing application examples, including several state-of-the-art reinforcement learning algorithms. Read more.
11:55am-12:35pm Thursday, September 6, 2018
Danny Lange (Unity Technologies)
Average rating: ****.
(4.67, 3 ratings)
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.
2:35pm-3:15pm Thursday, September 6, 2018
Implementing AI
Location: Continental 1-3
Jian Wu (NIO)
Jian Wu discusses an end-to-end engineering project to train and evaluate deep Q-learning models for targeting sequential marketing campaigns using the 10-fold cross-validation method. Jian also explains how to evaluate trained DQN models with neural network-based baseline models and shows that trained deep Q-learning models generally produce better-optimized long-term rewards. Read more.
2:35pm-3:15pm Thursday, September 6, 2018
Location: Imperial A
Woj Zaremba (OpenAI)
Woj Zaremba discusses deep reinforcement learning for robotics. Read more.
4:00pm-4:40pm Thursday, September 6, 2018
Location: Continental 1-3
Danny Goodman (Switchback Ventures)
Danny Goodman discusses reinforcement learning and the future of software. Read more.
4:50pm-5:30pm Thursday, September 6, 2018
Implementing AI
Location: Imperial B
Cathy Wu (UC Berkeley)
Using novel techniques in model-free deep reinforcement learning and control theory, Cathy Wu explores and quantifies the potential impact of a small fraction of automated vehicles on low-level traffic flow dynamics, such as congestion on a variety of important traffic contexts. Read more.
11:05am-11:45am Friday, September 7, 2018
Risto Miikkulainen (
Average rating: *****
(5.00, 1 rating)
Deep learning (DL) has transformed much of AI and demonstrated how machine learning can make a difference in the real world. With DL, the massive expansion of available training data and compute gave neural networks a new instantiation that significantly increased their power. Evolutionary computation (EC) is on the verge of a similar breakthrough. Risto Miikkulainen explains why. Read more.
11:55am-12:35pm Friday, September 7, 2018
Implementing AI
Location: Yosemite BC
Mark Hammond (Microsoft)
Average rating: ****.
(4.00, 1 rating)
Building complex, real-world reinforcement learning systems requires leveraging techniques such as curriculum learning, hierarchical RL, and reward shaping. Mark Hammond explores many of these techniques and illustrates how they can be effectively combined into a comprehensive machine teaching program. Read more.
2:35pm-3:15pm Friday, September 7, 2018
Location: Imperial A
Sergey Levine (UC Berkeley)
Average rating: ****.
(4.00, 1 rating)
Sergey Levine shares techniques in reinforcement learning that allow you to tackle sequential decision-making problems that arise across a range of real-world deployments of artificial intelligence systems and explains how emerging technologies in meta-learning make it possible for deep learning systems to learn from even small amounts of data. Read more.
4:50pm-5:30pm Friday, September 7, 2018
Varun Arora (Baidu USA)
We haven't figured out how to make the perfect robot tutors. But we have figured out how make them much more effective in improving student learning outcomes with modern AI techniques. Varun Arora covers some of those important techniques, along with real-world examples. Read more.