-
1
-
-
0003091684
-
Convergence problems of general-sum multiagent reinforcement learning
-
M. Bowling. Convergence problems of general-sum multiagent reinforcement learning. In Proceedings of ICML-00, pages 89-94, 2000.
-
(2000)
Proceedings of ICML-00
, pp. 89-94
-
-
Bowling, M.1
-
2
-
-
0036531878
-
Multiagent learning using a variable learning rate
-
M. Bowling and M. Veloso. Multiagent learning using a variable learning rate. Artificial Intelligence, 136:215-250, 2002.
-
(2002)
Artificial Intelligence
, vol.136
, pp. 215-250
-
-
Bowling, M.1
Veloso, M.2
-
3
-
-
84898960502
-
Playing is believing: The role of beliefs in multi-agent learning
-
MIT Press
-
Y.-H. Chang and L. P. Kaelbling. Playing is believing: The role of beliefs in multi-agent learning. In Proceedings of NIPS-2001. MIT Press, 2002.
-
(2002)
Proceedings of NIPS-2001
-
-
Chang, Y.-H.1
Kaelbling, L.P.2
-
4
-
-
78149326576
-
Multiplicative adjustment of class probability: Educating naive bayes
-
IBM Research
-
S. J. Hong, J. Hosking, and R. Natarajan. Multiplicative adjustment of class probability: Educating naive Bayes. Technical Report RC-22393, IBM Research, 2002.
-
(2002)
Technical Report RC-22393
-
-
Hong, S.J.1
Hosking, J.2
Natarajan, R.3
-
5
-
-
0000929496
-
Multiagent reinforcement learning: Theoretical framework and an algorithm
-
Morgan Kaufmann
-
J. Hu and M. P. Wellman. Multiagent reinforcement learning: Theoretical framework and an algorithm. In Proceedings of ICML-98, pages 242-250. Morgan Kaufmann, 1998.
-
(1998)
Proceedings of ICML-98
, pp. 242-250
-
-
Hu, J.1
Wellman, M.P.2
-
6
-
-
31144466138
-
Efficient nash computation in large population games with bounded influence
-
M. Kearns and Y. Mansour. Efficient Nash computation in large population games with bounded influence. In Proceedings of UAI-02, pages 259-266, 2002.
-
(2002)
Proceedings of UAI-02
, pp. 259-266
-
-
Kearns, M.1
Mansour, Y.2
-
7
-
-
85149834820
-
Markov games as a framework for multi-agent reinforcement learning
-
Morgan Kaufmann
-
M. L. Littman. Markov games as a framework for multi-agent reinforcement learning. In Proceedings of ICML-94, pages 157-163. Morgan Kaufmann, 1994.
-
(1994)
Proceedings of ICML-94
, pp. 157-163
-
-
Littman, M.L.1
-
8
-
-
0242466944
-
Friend-or-foe Q-learning in general-sum games
-
Morgan Kaufmann
-
M. L. Littman. Friend-or-Foe Q-learning in general-sum games. In Proceedings of ICML-01. Morgan Kaufmann, 2001.
-
(2001)
Proceedings of ICML-01
-
-
Littman, M.L.1
-
9
-
-
0039225090
-
A convergent reinforcement learning algorithm in the continuous case based on a finite difference method
-
Morgan Kaufman
-
R. Munos. A convergent reinforcement learning algorithm in the continuous case based on a finite difference method. In Proceedings of IJCAI-97, pages 826-831. Morgan Kaufman, 1997.
-
(1997)
Proceedings of IJCAI-97
, pp. 826-831
-
-
Munos, R.1
-
10
-
-
0001644761
-
Nash convergence of gradient dynamics in general-sum games
-
Morgan Kaufman
-
S. Singh, M. Kearns, and Y. Mansour. Nash convergence of gradient dynamics in general-sum games. In Proceedings of UAI-2000, pages 541-548. Morgan Kaufman, 2000.
-
(2000)
Proceedings of UAI-2000
, pp. 541-548
-
-
Singh, S.1
Kearns, M.2
Mansour, Y.3
-
11
-
-
0001898381
-
Practical reinforcement learning in continuous spaces
-
W. D. Smart and L. P. Kaelbling. Practical reinforcement learning in continuous spaces. In Proceedings of ICML-00, pages 903-910, 2000.
-
(2000)
Proceedings of ICML-00
, pp. 903-910
-
-
Smart, W.D.1
Kaelbling, L.P.2
-
12
-
-
0031636218
-
Tree based discretization for continuous state space reinforcement learning
-
W. T. B. Uther and M. M. Veloso. Tree based discretization for continuous state space reinforcement learning. In Proceedings of AAAI-98, pages 769-774, 1998.
-
(1998)
Proceedings of AAAI-98
, pp. 769-774
-
-
Uther, W.T.B.1
Veloso, M.M.2
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