Nan Jiang

TalkRL: The Reinforcement Learning Podcast

Nan Jiang is an Assistant Professor of Computer Science at University of Illinois. He was a Postdoc Microsoft Research, and did his PhD at University of Michigan under Professor Satinder Singh.

Featured References

• Reinforcement Learning: Theory and Algorithms
  Alekh Agarwal Nan Jiang Sham M. Kakade

• Model-based RL in Contextual Decision Processes: PAC bounds and Exponential Improvements over Model-free Approaches
  Wen Sun, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford

• Information-Theoretic Considerations in Batch Reinforcement Learning
  Jinglin Chen, Nan Jiang

Additional References

• Towards a Unified Theory of State Abstraction for MDPs, Lihong Li, Thomas J. Walsh, Michael L. Littman
• Doubly Robust Off-policy Value Evaluation for Reinforcement Learning, Nan Jiang, Lihong Li
• Minimax Confidence Interval for Off-Policy Evaluation and Policy Optimization, Nan Jiang, Jiawei Huang
• Empirical Study of Off-Policy Policy Evaluation for Reinforcement Learning, Cameron Voloshin, Hoang M. Le, Nan Jiang, Yisong Yue

Errata

• [Robin] I misspoke when I said in domain randomization we want the agent to "ignore" domain parameters. What I should have said is, we want the agent to perform well within some range of domain parameters, it should be robust with respect to domain parameters.