Explaining intelligent behavior in biological organisms has been one of holy grails of artificial intelligence (AI) research. Reinforcement learning (RL) started out as a model of learning in biological systems and today has grown to be one of the important paradigms of intelligent system design drawing ideas from varied fields such as neuroscience, psychology, control theory, operations research and AI. In turn reinforcement learning has had significant impact in many domains – it is the most popular model of learning in computational neuroscience and is used to explain different phenomena observed in the brain; reinforcement learning methods have been used to build AI agents in domains which were traditionally regarded as very hard, such as the game of Go; RL has changed the traditional approach to adaptive optimal control theory by introducing newer ways of modeling system dynamics; and in robotics, RL is the primary learning paradigm used for training autonomous agents. From the early beginnings as a theory of behavioral psychology, over three decades RL has grown into a mathematically sophisticated field with rigorous underpinnings drawn from different disciplines. This workshop will introduce the participants to the basic concepts of reinforcement learning as well as more recent exciting results in the field from the leaders in the community.
Monday, March 23 | |||
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09:00 - 10:30 |
Richard Sutton University of Alberta |
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Video |
10:30 - 11:00 | Coffee Break | ||
11:00 - 12:30 |
Csaba Szepesvári University of Alberta |
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Video |
12:30 - 14:00 | Lunch | ||
14:00 - 15:30 |
Sridhar Mahadevan University of Massachusetts Amherst |
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15:30 - 16:00 | Coffee Break | ||
16:00 - 17:30 |
Vivek Borkar Indian Institute of Technology Bombay |
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slides Video |
Tuesday, March 24 | |||
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09:00 - 10:30 |
Richard Sutton University of Alberta |
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Video |
10:30 - 11:00 | Coffee Break | ||
11:00 - 12:30 |
Csaba Szepesvári University of Alberta |
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Video |
12:30 - 14:00 | Lunch | ||
14:00 - 15:30 |
Sridhar Mahadevan University of Massachusetts Amherst |
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Video |
15:30 - 16:00 | Coffee Break | ||
16:00 - 17:30 |
Shivaram Kalyanakrishnan Indian Institute of Technology Bombay |
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Wednesday, March 25 | |||
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09:00 - 10:30 |
Richard Sutton University of Alberta |
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Video |
10:30 - 11:00 | Coffee Break | ||
11:00 - 12:30 |
Csaba Szepesvári University of Alberta |
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Video |
12:30 - 14:00 | Lunch | ||
14:00 - 15:30 |
Sridhar Mahadevan University of Massachusetts Amherst |
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Video |
15:30 - 16:00 | Coffee Break | ||
16:00 - 17:30 |
Mausam Indian Institute of Technology Delhi |
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slides Video |
Thursday, March 26 | |||
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09:00 - 10:30 |
Satinder Singh University of Michigan Ann Arbor |
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Video |
10:30 - 11:00 | Coffee Break | ||
11:00 - 12:30 |
Shivaram Kalyanakrishnan Indian Institute of Technology Bombay |
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12:30 - 14:00 | Lunch | ||
14:00 - 15:30 |
Aditya Gopalan Indian Institute of Science Bangalore |
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Video |
15:30 - 16:00 | Coffee Break | ||
16:00 - 17:30 |
Satinder Singh University of Michigan Ann Arbor |
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Video |
Friday, March 27 | |||
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09:00 - 10:30 |
Satinder Singh University of Michigan Ann Arbor |
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Video |
10:30 - 11:00 | Coffee Break | ||
11:00 - 12:30 |
Shalabh Bhatnagar Indian Institute of Science Bangalore |
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Video |
12:30 - 14:00 | Lunch | ||
14:00 - 15:30 |
Aditya Gopalan Indian Institute of Science Bangalore |
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Video |
15:30 - 16:00 | Coffee Break | ||
16:00 - 17:30 |
Prashanth L.A |
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slides Video |
Saturday, March 28 | |||
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09:00 - 10:30 |
Balaraman Ravindran Indian Institute of Technology Madras |
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Video |
10:30 - 11:00 | Coffee Break | ||
11:00 - 12:30 |
Shalabh Bhatnagar Indian Institute of Science Bangalore |
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Video |
12:30 - 14:00 | Lunch |
Vivek Borkar
Sridhar Mahadevan
Satinder Singh
Richard Sutton
Csaba Szepesvari
Shalabh Bhatnagar
Aditya Gopalan
Shivaram Kalyanakrishnan
Balaraman Ravindran
Shalabh Bhatnagar
Indian Institute of ScienceBalaraman Ravindran
Indian Institute of Technology, Madras