Thomas J. Walsh
Thomas J. Walsh
Sony AI
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Towards a unified theory of state abstraction for MDPs.
L Li, TJ Walsh, ML Littman
AI&M 1 (2), 3, 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
Security considerations for voice over IP systems
DR Kuhn, TJ Walsh, S Fries
NIST special publication 800, 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
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
Towards Measuring Similarity in Description Logics.
A Borgida, TJ Walsh, H Hirsh
Description Logics 147, 1-8, 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
Challenges in securing voice over IP
TJ Walsh, DR Kuhn
IEEE Security & Privacy 3 (3), 44-49, 2005
Exploring compact reinforcement-learning representations with linear regression
TJ Walsh, I Szita, C Diuk, ML Littman
arXiv preprint arXiv:1205.2606, 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
Efficient learning of action schemas and web-service descriptions.
TJ Walsh, ML Littman
AAAI 8, 714-719, 2008
Reinforcement learning with multi-fidelity simulators
M Cutler, TJ Walsh, JP How
2014 IEEE International Conference on Robotics and Automation (ICRA), 3888-3895, 2014
Real-world reinforcement learning via multifidelity simulators
M Cutler, TJ Walsh, JP How
IEEE Transactions on Robotics 31 (3), 655-671, 2015
Sample efficient reinforcement learning with gaussian processes
R Grande, T Walsh, J How
International Conference on Machine Learning, 1332-1340, 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
Transferring state abstractions between MDPs
TJ Walsh, L Li, ML Littman
ICML Workshop on Structural Knowledge Transfer for Machine Learning, 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
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
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
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
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