| Towards a unified theory of state abstraction for MDPs. L Li, TJ Walsh, ML Littman AI&M 1 (2), 3, 2006 | 636 | 2006 |
| Outracing champion Gran Turismo drivers with deep reinforcement learning PR Wurman, S Barrett, K Kawamoto, J MacGlashan, K Subramanian, ... Nature 602 (7896), 223-228, 2022 | 432 | 2022 |
| Security considerations for voice over IP systems DR Kuhn, TJ Walsh, S Fries NIST special publication 800, 2005 | 314 | 2005 |
| Knows what it knows: a framework for self-aware learning L Li, ML Littman, TJ Walsh Proceedings of the 25th international conference on Machine learning, 568-575, 2008 | 301 | 2008 |
| A tutorial on linear function approximators for dynamic programming and reinforcement learning A Geramifard, TJ Walsh, S Tellex, G Chowdhary, N Roy, JP How Foundations and Trends® in Machine Learning 6 (4), 375-451, 2013 | 166 | 2013 |
| Towards Measuring Similarity in Description Logics. A Borgida, TJ Walsh, H Hirsh Description Logics 147, 1-8, 2005 | 159 | 2005 |
| Integrating sample-based planning and model-based reinforcement learning T Walsh, S Goschin, M Littman Proceedings of the aaai conference on artificial intelligence 24 (1), 612-617, 2010 | 141 | 2010 |
| Challenges in securing voice over IP TJ Walsh, DR Kuhn IEEE Security & Privacy 3 (3), 44-49, 2005 | 140 | 2005 |
| Exploring compact reinforcement-learning representations with linear regression TJ Walsh, I Szita, C Diuk, ML Littman arXiv preprint arXiv:1205.2606, 2012 | 139 | 2012 |
| Learning and planning in environments with delayed feedback TJ Walsh, A Nouri, L Li, ML Littman Autonomous Agents and Multi-Agent Systems 18, 83-105, 2009 | 102 | 2009 |
| Reinforcement learning with multi-fidelity simulators M Cutler, TJ Walsh, JP How 2014 IEEE International Conference on Robotics and Automation (ICRA), 3888-3895, 2014 | 94 | 2014 |
| Efficient learning of action schemas and web-service descriptions. TJ Walsh, ML Littman AAAI 8, 714-719, 2008 | 91 | 2008 |
| Real-world reinforcement learning via multifidelity simulators M Cutler, TJ Walsh, JP How IEEE Transactions on Robotics 31 (3), 655-671, 2015 | 84 | 2015 |
| Sample efficient reinforcement learning with gaussian processes R Grande, T Walsh, J How International Conference on Machine Learning, 1332-1340, 2014 | 79 | 2014 |
| Bayesian nonparametric reward learning from demonstration B Michini, TJ Walsh, AA Agha-Mohammadi, JP How IEEE Transactions on Robotics 31 (2), 369-386, 2015 | 70 | 2015 |
| Transferring state abstractions between MDPs TJ Walsh, L Li, ML Littman ICML Workshop on Structural Knowledge Transfer for Machine Learning, 2006 | 64 | 2006 |
| Democratic approximation of lexicographic preference models F Yaman, TJ Walsh, ML Littman, M Desjardins Proceedings of the 25th international Conference on Machine Learning, 1200-1207, 2008 | 56 | 2008 |
| Security considerations for voice over IP systems: Recommendations of the National Institute of Standards and Technology DR Kuhn, TJ Walsh, S Fries US Department of Commerce, Technology Administration, National Institute of …, 2005 | 48 | 2005 |
| Off-policy reinforcement learning with gaussian processes G Chowdhary, M Liu, R Grande, T Walsh, J How, L Carin IEEE/CAA Journal of Automatica Sinica 1 (3), 227-238, 2014 | 44 | 2014 |
| Towards understanding how humans teach robots T Kaochar, RT Peralta, CT Morrison, IR Fasel, TJ Walsh, PR Cohen User Modeling, Adaption and Personalization: 19th International Conference …, 2011 | 39 | 2011 |
