Department of Computer Science

Machine Learning Research Group

University of Texas at Austin Artificial Intelligence Lab

Publications: Reinforcement Learning

Reinforcement Learning tasks are learning problems where the desired behavior is not known; only sparse feedback on how well the agent is doing is provided. Reinforcement Learning techniques include value-function and policy iteration methods (note that although evolutionary computation and neuroevolution can also be seen as reinforcement learning methods, they are presented separately in this area hierarchy.)
  1. Learning a Policy for Opportunistic Active Learning
    [Details] [PDF] [Slides (PPT)] [Slides (PDF)]
    Aishwarya Padmakumar, Peter Stone, Raymond J. Mooney
    To Appear In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP-18), Brussels, Belgium, November 2018.
  2. Integrated Learning of Dialog Strategies and Semantic Parsing
    [Details] [PDF] [Slides (PPT)] [Slides (PDF)]
    Aishwarya Padmakumar and Jesse Thomason and Raymond J. Mooney
    In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2017), 547--557, Valencia, Spain, April 2017.
  3. Using Active Relocation to Aid Reinforcement Learning
    [Details] [PDF]
    Lilyana Mihalkova and Raymond Mooney
    In Prodeedings of the 19th International FLAIRS Conference (FLAIRS-2006), 580-585, Melbourne Beach, FL, May 2006.
  4. Guiding a Reinforcement Learner with Natural Language Advice: Initial Results in RoboCup Soccer
    [Details] [PDF]
    Gregory Kuhlmann, Peter Stone, Raymond J. Mooney, and Jude W. Shavlik
    In The AAAI-2004 Workshop on Supervisory Control of Learning and Adaptive Systems, July 2004.