Reinforcement learning for call admission control and routing in integrated service networks

Advances in Neural Information Processing Systems

Volumn , Issue , 1998, Pages 922-928

  Marbach, Peter ^a   Mihatsch, Oliver ^b   Schulte, Miriam ^c   Tsitsiklis, John N ^a  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^b SIEMENS AG   (Germany)

^c TECHNICAL UNIVERSITY OF MUNICH   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

COMPLEX NETWORKS; REINFORCEMENT LEARNING;

DECOMPOSITION APPROACH; DYNAMIC PROGRAMMING PROBLEMS; FEATURE CONFIGURATION; HEURISTIC POLICIES; INTEGRATED SERVICE; INTEGRATED SERVICE NETWORKS; NETWORK RESOURCE; ROUTING POLICIES;

TELECOMMUNICATION NETWORKS;

EID: 84898959706     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (6)

1. Bertsekas, D. P. (1995) Dynamic Programming and Optimal Control. Athena Scientific, Belmont, MA.
2. Crites, R. H., Barto, A. G. (1996) Improving elevator performance using reinforcement learning. In D. S. Touretzky, M. C. Mozer and M. E. Hasselmo (eds.), Advances in Neural Information Processing Systems 8, pp. 1017-1023. Cambridge, MA: MIT Press.
3. Singh, S., Bertsekas, D. P. (1997) Reinforcement learning for dynamic channel allocation in cellular telephone systems. To appear in Advances in Neural Information Processing Systems 9, Cambridge, MA: MIT Press.
4. Sutton, R. S. (1988) Learning to predict by the method of temporal differences. Machine Learning, 3:9-44.
5. Tesauro, G. J. (1992) Practical issues in temporal difference learning. Machine Learning, 8(3/4):257-277.
6. Zhang, W., Dietterich, T. G. (1996) High performance job-shop scheduling with a time- delay TD(A) network. In D. S. Touretzky, M. C. Mozer and M. E. Hasselmo (eds.), Advances in Neural Information Processing Systems 8, pp. 1024-1030. Cambridge, MA: MIT Press.