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).

Show abstracts
  1. Using Natural Language to Aid Task Specification in Sequential Decision Making Problems
    [Details] [PDF] [Slides (PDF)] [Video]
    Prasoon Goyal
    October 2021. Ph.D. Proposal.
  2. Supervised Attention from Natural Language Feedback for Reinforcement Learning
    [Details] [PDF]
    Clara Cecilia Cannon
    Masters Thesis, Department of Computer Science, The University of Texas at Austin, May 2021.
  3. Dialog Policy Learning for Joint Clarification and Active Learning Queries
    [Details] [PDF] [Slides (PDF)] [Poster] [Video]
    Aishwarya Padmakumar, Raymond J. Mooney
    In The AAAI Conference on Artificial Intelligence (AAAI), February 2021.
  4. Dialog as a Vehicle for Lifelong Learning of Grounded Language Understanding Systems
    [Details] [PDF] [Slides (PDF)]
    Aishwarya Padmakumar
    PhD Thesis, Department of Computer Science, The University of Texas at Austin, August 2020.
  5. PixL2R: Guiding Reinforcement Learning using Natural Language by Mapping Pixels to Rewards
    [Details] [PDF]
    Prasoon Goyal, Scott Niekum, Raymond J. Mooney
    In 4th Conference on Robot Learning (CoRL), November 2020. Also presented on the 1st Language in Reinforcement Learning (LaReL) Workshop at ICML, July 2020 (Best Paper Award), the 6th Deep Reinforcement Learning Workshop at Neural Information Processing Systems (NeurIPS), Dec 2020.
  6. Evaluating the Robustness of Natural Language Reward Shaping Models to Spatial Relations
    [Details] [PDF] [Slides (PPT)] [Slides (PDF)]
    Antony Yun
    May 2020. Undergraduate Honors Thesis, Computer Science Department, University of Texas at Austin.
  7. Using Natural Language for Reward Shaping in Reinforcement Learning
    [Details] [PDF] [Slides (PDF)] [Poster]
    Prasoon Goyal, Scott Niekum, Raymond J. Mooney
    In Proceedings of the 28th International Joint Conference on Artificial Intelligence, Macao, China, August 2019.
  8. Learning a Policy for Opportunistic Active Learning
    [Details] [PDF]
    Aishwarya Padmakumar, Peter Stone, Raymond J. Mooney
    In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP-18), Brussels, Belgium, November 2018.
  9. Integrated Learning of Dialog Strategies and Semantic Parsing
    [Details] [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.
  10. 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.
  11. 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.