TalkRL: The Reinforcement Learning Podcast

Nan Jiang


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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. 


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TalkRL: The Reinforcement Learning PodcastBy Robin Ranjit Singh Chauhan

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