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. 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.
  2. 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.
  3. 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.