-
1
-
-
78649507911
-
A Bayesian sampling approach to exploration in reinforcement learning
-
Asmuth, J., Li, L., Littman, M., Nouri, A., & Wingate, D. (2009). A Bayesian sampling approach to exploration in reinforcement learning. Proceedings of the 25th Conference on Uncertainty in Artifical Intelligence (UAI-09).
-
(2009)
Proceedings of the 25th Conference on Uncertainty in Artifical Intelligence (UAI-09)
-
-
Asmuth, J.1
Li, L.2
Littman, M.3
Nouri, A.4
Wingate, D.5
-
3
-
-
0036568025
-
Finite-time analysis of the multiarmed bandit problem
-
DOI 10.1023/A:1013689704352, Computational Learning Theory
-
Auer, P., Cesa-Bianchi, N., & Fischer, P. (2002). Finite-time analysis of the multiarmed bandit problem. Machine Learning, 47, 235-256. (Pubitemid 34126111)
-
(2002)
Machine Learning
, vol.47
, Issue.2-3
, pp. 235-256
-
-
Auer, P.1
Cesa-Bianchi, N.2
Fischer, P.3
-
4
-
-
0041965975
-
R-MAX-A general polynomial time algorithm for near-optimal reinforcement learning
-
Brafman, R. I., & Tennenholtz, M. (2002). R-MAX-A general polynomial time algorithm for near-optimal reinforcement learning. Journal of Machine Learning Research, 3, 213-231.
-
(2002)
Journal of Machine Learning Research
, vol.3
, pp. 213-231
-
-
Brafman, R.I.1
Tennenholtz, M.2
-
8
-
-
23244466805
-
-
Doctoral dissertation, Gatsby Computational Neuroscience Unit, University College London
-
Kakade, S. M. (2003). On the sample complexity of reinforcement learning. Doctoral dissertation, Gatsby Computational Neuroscience Unit, University College London.
-
(2003)
On the Sample Complexity of Reinforcement Learning
-
-
Kakade, S.M.1
-
11
-
-
71149109483
-
Near-Bayesian exploration in polynomial time
-
New York, NY, USA: ACM
-
Kolter, J. Z., & Ng, A. Y. (2009). Near-Bayesian exploration in polynomial time. Proceedings of the 26th Annual International Conference on Machine Learning (pp. 513-520). New York, NY, USA: ACM.
-
(2009)
Proceedings of the 26th Annual International Conference on Machine Learning
, pp. 513-520
-
-
Kolter, J.Z.1
Ng, A.Y.2
-
15
-
-
77950032550
-
Markov chain sampling methods for dirichlet process mixture models
-
Neal, R. M. (2000). Markov chain sampling methods for dirichlet process mixture models. Journal of Computational and Graphical Statistics, Vol. 9, pp. 249-265.
-
(2000)
Journal of Computational and Graphical Statistics
, vol.9
, pp. 249-265
-
-
Neal, R.M.1
-
16
-
-
33749251297
-
An analytic solution to discrete Bayesian reinforcement learning
-
Poupart, P., Vlassis, N., Hoey, J., & Regan, K. (2006). An analytic solution to discrete Bayesian reinforcement learning. Proceedings of the 23rd International Conference on Machine Learning (pp. 697-704).
-
(2006)
Proceedings of the 23rd International Conference on Machine Learning
, pp. 697-704
-
-
Poupart, P.1
Vlassis, N.2
Hoey, J.3
Regan, K.4
-
21
-
-
55549110436
-
An analysis of modelbased interval estimation for Markov decision processes
-
Special Issue on Learning Theory
-
Strehl, A. L., & Littman, M. L. (2008). An analysis of modelbased interval estimation for Markov decision processes. Journal of Computer and System Sciences, 74, 1309-1331. Special Issue on Learning Theory.
-
(2008)
Journal of Computer and System Sciences
, vol.74
, pp. 1309-1331
-
-
Strehl, A.L.1
Littman, M.L.2
-
25
-
-
79958846996
-
Exploring compact reinforcement-learning representations with linear regression
-
Arlington, Virginia, United States: AUAI Press
-
Walsh, T. J., Szita, I., Diuk, C., & Littman, M. L. (2009). Exploring compact reinforcement-learning representations with linear regression. Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (pp. 591-598). Arlington, Virginia, United States: AUAI Press.
-
(2009)
Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
, pp. 591-598
-
-
Walsh, T.J.1
Szita, I.2
Diuk, C.3
Littman, M.L.4
-
26
-
-
31844436266
-
Bayesian sparse sampling for on-line reward optimization
-
New York, NY, USA: ACM
-
Wang, T., Lizotte, D., Bowling, M., & Schuurmans, D. (2005). Bayesian sparse sampling for on-line reward optimization. ICML '05: Proceedings of the 22nd International Conference on Machine Learning (pp. 956-963). New York, NY, USA: ACM.
-
(2005)
ICML '05: Proceedings of the 22nd International Conference on Machine Learning
, pp. 956-963
-
-
Wang, T.1
Lizotte, D.2
Bowling, M.3
Schuurmans, D.4
-
27
-
-
34547994508
-
Multi-task reinforcement learning: A hierarchical Bayesian approach. Machine Learning
-
Wilson, A., Fern, A., Ray, S., & Tadepalli, P. (2007). Multi-task reinforcement learning: A hierarchical Bayesian approach. Machine Learning, Proceedings of the Twenty-Fourth International Conference (ICML 2007) (pp. 1015-1022).
-
(2007)
Proceedings of the Twenty-Fourth International Conference (ICML 2007)
, pp. 1015-1022
-
-
Wilson, A.1
Fern, A.2
Ray, S.3
Tadepalli, P.4
|