Times are listed in Pacific Time.

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Opening Remarks
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Planning and Markov Decision Processes (Part 1)
Csaba Szepesvari (University of Alberta, Google DeepMind), Mengdi Wang (Princeton University, Google DeepMind)
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Break
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Planning and Markov Decision Processes (Part 2)
Csaba Szepesvari (University of Alberta, Google DeepMind), Mengdi Wang (Princeton University, Google DeepMind)
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Lunch
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Online Learning and Bandits (Part 1)
Alan Malek (DeepMind), Wouter Koolen (Centrum Wiskunde & Informatica)
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Break
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Online Learning and Bandits (Part 2)
Alan Malek (DeepMind), Wouter Koolen (Centrum Wiskunde & Informatica)
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Coffee Break
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Optimizing Intended Reward Functions: Extracting All the Right Information From All the Right Places
Anca Dragan (UC Berkeley)
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Online Learning in MDPs (Part 1)
Ambuj Tewari (University of Michigan)
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Break
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Online Learning in MDPs (Part 2)
Gergely Neu (UPF)
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Lunch
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Batch (Offline) RL (Part 1)
Emma Brunskill (Stanford University)
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Break
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Batch (Offline) RL (Part 2)
Emma Brunskill (Stanford University)
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Coffee Break
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The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies
Stephan Zheng (Salesforce Research)
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Statistical Considerations in Reinforcement Learning (Part 1): Statistical Inference and Non-Regularity
Eric Laber (North Carolina State University)
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Break
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Statistical Considerations in Reinforcement Learning (Part 1): Statistical Inference and Non-Regularity
Eric Laber (North Carolina State University)
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Lunch
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Statistical Considerations in Reinforcement Learning (Part 2): Emerging Application Areas and Challenges
Eric Laber (North Carolina State University)
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Break
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Statistical Considerations in Reinforcement Learning (Part 2): Emerging Application Areas and Challenges
Eric Laber (North Carolina State University)
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Coffee Break
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Learning to Act from Observations
Ashley Edwards (Georgia Tech)
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Control Fundamentals
Sean Meyn (University of Florida)
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Break
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Every Optimization Problem Is a Quadratic Program: Applications to Dynamic Programming and Q-Learning
Sean Meyn (University of Florida)
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Lunch
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Basics of Algorithm Design and Analysis
Sean Meyn (University of Florida)
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Break
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Recent Results on RL With Gradient Free Optimization
Sean Meyn (University of Florida)
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Coffee Break
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Gradient-Free Optimization With Applications to Power Systems
Andrey Bernstein (National Renewable Energy Laboratory)
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Stochastic Programming Approach to Optimization Under Uncertainty (Part 1)
Alex Shapiro (Georgia Tech)
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Break
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Stochastic Programming Approach to Optimization Under Uncertainty (Part 2)
Alex Shapiro (Georgia Tech)
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Lunch
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Simulation Methodology: An Overview (Part 1)
Peter Glynn (Stanford)
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Break
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Simulation Methodology: An Overview (Part 2)
Peter Glynn (Stanford)
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Coffee Break
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A Few Challenge Problems from Robotics
Russ Tedrake (MIT & Toyota Research Institute)