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Theory of Reinforcement Learning Boot Camp
Program
Theory of Reinforcement Learning
Location
https://berkeley.zoom.us/j/92251291688
Date
Monday, Aug. 31
–
Friday, Sept. 4, 2020
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The Workshop
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Times are listed in
Pacific Time.
Monday, Aug. 31, 2020
8:50
–
9 a.m.
Opening Remarks
9
–
10 a.m.
Planning and Markov Decision Processes (Part 1)
Csaba Szepesvari (University of Alberta, Google DeepMind)
,
Mengdi Wang (Princeton University, Google DeepMind)
10
–
10:30 a.m.
Break
10:30
–
11:30 a.m.
Planning and Markov Decision Processes (Part 2)
Csaba Szepesvari (University of Alberta, Google DeepMind)
,
Mengdi Wang (Princeton University, Google DeepMind)
11:30 a.m.
–
12:30 p.m.
Lunch
12:30
–
1:30 p.m.
Online Learning and Bandits (Part 1)
Alan Malek (DeepMind)
,
Wouter Koolen (Centrum Wiskunde & Informatica)
1:30
–
2 p.m.
Break
2
–
3 p.m.
Online Learning and Bandits (Part 2)
Alan Malek (DeepMind)
,
Wouter Koolen (Centrum Wiskunde & Informatica)
3
–
3:30 p.m.
Coffee Break
3:30
–
4:30 p.m.
Optimizing Intended Reward Functions: Extracting All the Right Information From All the Right Places
Anca Dragan (UC Berkeley)
Tuesday, Sept. 1, 2020
9
–
10 a.m.
Online Learning in MDPs (Part 1)
Ambuj Tewari (University of Michigan)
10
–
10:30 a.m.
Break
10:30
–
11:30 a.m.
Online Learning in MDPs (Part 2)
Gergely Neu (UPF)
11:30 a.m.
–
12:30 p.m.
Lunch
12:30
–
1:30 p.m.
Batch (Offline) RL (Part 1)
Emma Brunskill (Stanford University)
1:30
–
2 p.m.
Break
2
–
3 p.m.
Batch (Offline) RL (Part 2)
Emma Brunskill (Stanford University)
3
–
3:30 p.m.
Coffee Break
3:30
–
4:30 p.m.
The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies
Stephan Zheng (Salesforce Research)
Wednesday, Sept. 2, 2020
9
–
10 a.m.
Statistical Considerations in Reinforcement Learning (Part 1): Statistical Inference and Non-Regularity
Eric Laber (North Carolina State University)
10
–
10:30 a.m.
Break
10:30
–
11:30 a.m.
Statistical Considerations in Reinforcement Learning (Part 1): Statistical Inference and Non-Regularity
Eric Laber (North Carolina State University)
11:30 a.m.
–
12:30 p.m.
Lunch
12:30
–
1:30 p.m.
Statistical Considerations in Reinforcement Learning (Part 2): Emerging Application Areas and Challenges
Eric Laber (North Carolina State University)
1:30
–
2 p.m.
Break
2
–
3 p.m.
Statistical Considerations in Reinforcement Learning (Part 2): Emerging Application Areas and Challenges
Eric Laber (North Carolina State University)
3
–
3:30 p.m.
Coffee Break
3:30
–
4:30 p.m.
Learning to Act from Observations
Ashley Edwards (Georgia Tech)
Thursday, Sept. 3, 2020
9
–
10 a.m.
Control Fundamentals
Sean Meyn (University of Florida)
10
–
10:30 a.m.
Break
10:30
–
11:30 a.m.
Every Optimization Problem Is a Quadratic Program: Applications to Dynamic Programming and Q-Learning
Sean Meyn (University of Florida)
11:30 a.m.
–
12:30 p.m.
Lunch
12:30
–
1:30 p.m.
Basics of Algorithm Design and Analysis
Sean Meyn (University of Florida)
1:30
–
2 p.m.
Break
2
–
3 p.m.
Recent Results on RL With Gradient Free Optimization
Sean Meyn (University of Florida)
3
–
3:30 p.m.
Coffee Break
3:30
–
4:30 p.m.
Gradient-Free Optimization With Applications to Power Systems
Andrey Bernstein (National Renewable Energy Laboratory)
Friday, Sept. 4, 2020
9
–
10 a.m.
Stochastic Programming Approach to Optimization Under Uncertainty (Part 1)
Alex Shapiro (Georgia Tech)
10
–
10:30 a.m.
Break
10:30
–
11:30 a.m.
Stochastic Programming Approach to Optimization Under Uncertainty (Part 2)
Alex Shapiro (Georgia Tech)
11:30 a.m.
–
12:30 p.m.
Lunch
12:30
–
1:30 p.m.
Simulation Methodology: An Overview (Part 1)
Peter Glynn (Stanford)
1:30
–
2 p.m.
Break
2
–
3 p.m.
Simulation Methodology: An Overview (Part 2)
Peter Glynn (Stanford)
3
–
3:30 p.m.
Coffee Break
3:30
–
4:30 p.m.
A Few Challenge Problems from Robotics
Russ Tedrake (MIT & Toyota Research Institute)
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