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Nan Jiang
MP3•Episode home
Manage episode 266378971 series 2536330
Content provided by Robin Ranjit Singh Chauhan. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Robin Ranjit Singh Chauhan or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.
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.
55 episodes
MP3•Episode home
Manage episode 266378971 series 2536330
Content provided by Robin Ranjit Singh Chauhan. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Robin Ranjit Singh Chauhan or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.
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.
55 episodes
All episodes
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