printer friendly pageCS 234: Reinforcement Learning
To realize the dreams and impact of AI requires autonomous systems that learn to make good decisions. Reinforcement learning is one powerful paradigm for doing so, and it is relevant to an enormous range of tasks, including robotics, game playing, consumer modeling and healthcare. This class will briefly cover background on Markov decision processes and reinforcement learning, before focusing on some of the central problems, including scaling up to large domains and the exploration challenge. One key tool for tackling complex RL domains is deep learning and this class will include at least one homework on deep reinforcement learning. Prerequisites: proficiency in python,
CS 229 or equivalents or permission of the instructor; linear algebra, basic probability.
Terms: Win
| Units: 3
Instructors:
Brunskill, E. (PI)
;
Badlani, R. (TA)
;
Deaderick, W. (TA)
...
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Instructors:
Brunskill, E. (PI)
;
Badlani, R. (TA)
;
Deaderick, W. (TA)
;
Liu, Y. (TA)
;
Mu, T. (TA)
;
Petit, B. (TA)
;
Thomas, G. (TA)
;
Xiong, Z. (TA)
;
Yuan, C. (TA)
;
Zanette, A. (TA)
CS 332: Advanced Survey of Reinforcement Learning
This class will provide a core overview of essential topics and new research frontiers in reinforcement learning. Planned topics include: model free and model based reinforcement learning, policy search, Monte Carlo Tree Search planning methods, off policy evaluation, exploration, imitation learning, temporal abstraction/hierarchical approaches, safety and risk sensitivity, human-in-the-loop RL, inverse reinforcement learning, learning to communicate, and insights from human learning. Students are expected to create an original research paper on a related topic. Prerequisites:
CS 221 or
AA 238/
CS 238 or
CS 234 or
CS 229 or similar experience.
Last offered: Autumn 2018
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